This paper considers the problem of differentially private semi-supervised transfer and multi-task learning. The notion of \emph{membership-mapping} has been developed using measure theory basis to learn data representation via a fuzzy membership function. An alternative conception of deep autoencoder, referred to as \emph{Conditionally Deep Membership-Mapping Autoencoder (CDMMA)}, is considered for transferrable deep learning. Under practice-oriented settings, an analytical solution for the learning of CDMMA can be derived by means of variational optimization. The paper proposes a transfer and multi-task learning approach that combines CDMMA with a tailored noise adding mechanism to achieve a given level of privacy-loss bound with the minimum perturbation of the data. Numerous experiments were carried out using MNIST, USPS, Office, and Caltech256 datasets to verify the competitive robust performance of the proposed methodology.  ( 2 min )

Dynamic graph embedding has gained great attention recently due to its capability of learning low dimensional graph representations for complex temporal graphs with high accuracy. However, recent advances mostly focus on learning node embeddings as deterministic "vectors" for static graphs yet disregarding the key graph temporal dynamics and the evolving uncertainties associated with node embedding in the latent space. In this work, we propose an efficient stochastic dynamic graph embedding method (DynG2G) that applies an inductive feed-forward encoder trained with node triplet-based contrastive loss. Every node per timestamp is encoded as a time-dependent probabilistic multivariate Gaussian distribution in the latent space, hence we can quantify the node embedding uncertainty on-the-fly. We adopted eight different benchmarks that represent diversity in size (from 96 nodes to 87,626 and from 13,398 edges to 4,870,863) and diversity in dynamics. We demonstrate via extensive experiments on these eight dynamic graph benchmarks that DynG2G achieves new state-of-the-art performance in capturing the underlying temporal node embeddings. We also demonstrate that DynG2G can predict the evolving node embedding uncertainty, which plays a crucial role in quantifying the intrinsic dimensionality of the dynamical system over time. We obtain a universal relation of the optimal embedding dimension, $L_o$, versus the effective dimensionality of uncertainty, $D_u$, and we infer that $L_o=D_u$ for all cases. This implies that the uncertainty quantification approach we employ in the DynG2G correctly captures the intrinsic dimensionality of the dynamics of such evolving graphs despite the diverse nature and composition of the graphs at each timestamp. Moreover, this $L_0 - D_u$ correlation provides a clear path to select adaptively the optimum embedding size at each timestamp by setting $L \ge D_u$.  ( 2 min )

In bandit with distribution shifts, one aims to automatically adapt to unknown changes in reward distribution, and restart exploration when necessary. While this problem has been studied for many years, a recent breakthrough of Auer et al. (2018, 2019) provides the first adaptive procedure to guarantee an optimal (dynamic) regret $\sqrt{LT}$, for $T$ rounds, and an unknown number $L$ of changes. However, while this rate is tight in the worst case, it remained open whether faster rates are possible, without prior knowledge, if few changes in distribution are actually severe. To resolve this question, we propose a new notion of significant shift, which only counts very severe changes that clearly necessitate a restart: roughly, these are changes involving not only best arm switches, but also involving large aggregate differences in reward overtime. Thus, our resulting procedure adaptively achieves rates always faster (sometimes significantly) than $O(\sqrt{ST})$, where $S\ll L$ only counts best arm switches, while at the same time, always faster than the optimal $O(V^{\frac{1}{3}}T^{\frac{2}{3}})$ when expressed in terms of total variation $V$ (which aggregates differences overtime). Our results are expressed in enough generality to also capture non-stochastic adversarial settings.  ( 2 min )

Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games, however, in more complex games (e.g. those with high branching factor or real-time ones), as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey has been published in 2012. Contributions that appeared since its release are of particular interest for this review.  ( 2 min )

Classification of sequences of temporal intervals is a part of time series analysis which concerns series of events. We propose a new method of transforming the problem to a task of multivariate series classification. We use one of the state-of-the-art algorithms from the latter domain on the new representation to obtain significantly better accuracy than the state-of-the-art methods from the former field. We discuss limitations of this workflow and address them by developing a novel method for classification termed COSTI (short for Classification of Sequences of Temporal Intervals) operating directly on sequences of temporal intervals. The proposed method remains at a high level of accuracy and obtains better performance while avoiding shortcomings connected to operating on transformed data. We propose a generalized version of the problem of classification of temporal intervals, where each event is supplemented with information about its intensity. We also provide two new data sets where this information is of substantial value.  ( 2 min )

This paper deals with the problem of localization in a cellular network in a dense urban scenario. Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between the devices and the satellites is low, and thus alternative localization methods are required for good accuracy. We present LocUNet: A fully convolutional, end-to-end trained neural network for the localization task, which merely depends on the received signal strengths (RSS) from Base Stations (BSs).In a wireless network, user devices scan the base station beacon slots and identify the few strongest base station signals for handover and user-base station association purposes. In the proposed method, the user to be localized simply reports such received signal strengths to a central processing unit, which may be located in the cloud. Alternatively, the localization can be performed locally at the user. Using the pathloss radio map estimations and the RSS measurements, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of the radio maps. The proposed method does not require pre-sampling of the environment; and is suitable for real-time applications, thanks to the RadioUNet, a neural network-based radio map estimator. Moreover, two novel datasets that allow for numerical evaluations of RSS and ToA methods in realistic urban environments are presented and set publicly available for the use of research community. By using these datasets, we also provided a fair comparison of state-of-the-art RSS and ToA-based methods in the dense urban scenario, LocUNet outperforming all the compared methods.  ( 2 min )

We analyse the performance of several iterative algorithms for the quantisation of a probability measure $\mu$, based on the minimisation of a Maximum Mean Discrepancy (MMD). Our analysis includes kernel herding, greedy MMD minimisation and Sequential Bayesian Quadrature (SBQ). We show that the finite-sample-size approximation error, measured by the MMD, decreases as $1/n$ for SBQ and also for kernel herding and greedy MMD minimisation when using a suitable step-size sequence. The upper bound on the approximation error is slightly better for SBQ, but the other methods are significantly faster, with a computational cost that increases only linearly with the number of points selected. This is illustrated by two numerical examples, with the target measure $\mu$ being uniform (a space-filling design application) and with $\mu$ a Gaussian mixture. They suggest that the bounds derived in the paper are overly pessimistic, in particular for SBQ. The sources of this pessimism are identified but seem difficult to counter.  ( 2 min )

Machine learning models with both good predictability and high interpretability are crucial for decision support systems. Linear regression is one of the most interpretable prediction models. However, the linearity in a simple linear regression worsens its predictability. In this work, we introduce a locally adaptive interpretable regression (LoAIR). In LoAIR, a metamodel parameterized by neural networks predicts percentile of a Gaussian distribution for the regression coefficients for a rapid adaptation. Our experimental results on public benchmark datasets show that our model not only achieves comparable or better predictive performance than the other state-of-the-art baselines but also discovers some interesting relationships between input and target variables such as a parabolic relationship between CO2 emissions and Gross National Product (GNP). Therefore, LoAIR is a step towards bridging the gap between econometrics, statistics, and machine learning by improving the predictive ability of linear regression without depreciating its interpretability.  ( 2 min )

Given that Neural Networks generalize unreasonably well in the IID setting (with benign overfitting and betterment in performance with more parameters), OOD presents a consistent failure case to better the understanding of how they learn. This paper focuses on Domain Generalization (DG), which is perceived as the front face of OOD generalization. We find that the presence of multiple domains incentivizes domain agnostic learning and is the primary reason for generalization in Tradition DG. We show that the state-of-the-art results can be obtained by borrowing ideas from IID generalization and the DG tailored methods fail to add any performance gains. Furthermore, we perform explorations beyond the Traditional DG (TDG) formulation and propose a novel ClassWise DG (CWDG) benchmark, where for each class, we randomly select one of the domains and keep it aside for testing. Despite being exposed to all domains during training, CWDG is more challenging than TDG evaluation. We propose a novel iterative domain feature masking approach, achieving state-of-the-art results on the CWDG benchmark. Overall, while explaining these observations, our work furthers insights into the learning mechanisms of neural networks.  ( 2 min )

Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise. In an attempt to placate this limitation techniques can be applied for evaluating a quantum circuit using a machine with fewer qubits than the circuit naively requires. These techniques work by evaluating many smaller circuits on the smaller machine, that are then combined in a polynomial to replicate the output of the larger machine. This scheme requires more circuit evaluations than are practical for general circuits. However, we investigate the possibility that for certain applications many of these subcircuits are superfluous, and that a much smaller sum is sufficient to estimate the full circuit. We construct a machine learning model that may be capable of approximating the outputs of the larger circuit with much fewer circuit evaluations. We successfully apply our model to the task of digit recognition, using simulated quantum computers much smaller than the data dimension. The model is also applied to the task of approximating a random 10 qubit PQC with simulated access to a 5 qubit computer, even with only relatively modest number of circuits our model provides an accurate approximation of the 10 qubit PQCs output, superior to a neural network attempt. The developed method might be useful for implementing quantum models on larger data throughout the NISQ era.  ( 2 min )

Recent work has shown that more effective dense retrieval models can be obtained by distilling ranking knowledge from an existing base re-ranking model. In this paper, we propose a generic curriculum learning based optimization framework called CL-DRD that controls the difficulty level of training data produced by the re-ranking (teacher) model. CL-DRD iteratively optimizes the dense retrieval (student) model by increasing the difficulty of the knowledge distillation data made available to it. In more detail, we initially provide the student model coarse-grained preference pairs between documents in the teacher's ranking and progressively move towards finer-grained pairwise document ordering requirements. In our experiments, we apply a simple implementation of the CL-DRD framework to enhance two state-of-the-art dense retrieval models. Experiments on three public passage retrieval datasets demonstrate the effectiveness of our proposed framework.  ( 2 min )

Understanding and modeling human behavior is fundamental to almost any computer vision and robotics applications that involve humans. In this thesis, we take a holistic approach to human behavior modeling and tackle its three essential aspects -- simulation, perception, and generation. Throughout the thesis, we show how the three aspects are deeply connected and how utilizing and improving one aspect can greatly benefit the other aspects. We also discuss the lessons learned and our vision for what is next for human behavior modeling.

We study compressive sensing with a deep generative network prior. Initial theoretical guarantees for efficient recovery from compressed linear measurements have been developed for signals in the range of a ReLU network with Gaussian weights and logarithmic expansivity: that is when each layer is larger than the previous one by a logarithmic factor. It was later shown that constant expansivity is sufficient for recovery. It has remained open whether the expansivity can be relaxed allowing for networks with contractive layers, as often the case of real generators. In this work we answer this question, proving that a signal in the range of a Gaussian generative network can be recovered from a few linear measurements provided that the width of the layers is proportional to the input layer size (up to log factors). This condition allows the generative network to have contractive layers. Our result is based on showing that Gaussian matrices satisfy a matrix concentration inequality, which we term Range Restricted Weight Distribution Condition (R2WDC), and weakens the Weight Distribution Condition (WDC) upon which previous theoretical guarantees were based on. The WDC has also been used to analyze other signal recovery problems with generative network priors. By replacing the WDC with the R2WDC, we are able to extend previous results for signal recovery with expansive generative network priors to non-expansive ones. We discuss these extensions for phase retrieval, denoising, and spiked matrix recovery.

The right to AI explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolation, which describes the extent to which AI models can be clueless when they encounter unfamiliar samples (i.e., samples outside the convex hull of their training sets, as we will explain). We report that AI models extrapolate outside their range of familiar data, frequently and without notifying the users and stakeholders. Knowing whether a model has extrapolated or not is a fundamental insight that should be included in explaining AI models in favor of transparency and accountability. Instead of dwelling on the negatives, we offer ways to clear the roadblocks in promoting AI transparency. Our analysis commentary accompanying practical clauses useful to include in AI regulations such as the National AI Initiative Act in the US and the AI Act by the European Commission.

Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust uncertainty quantification remains a challenge. This has been addressed with variational autoencoders so far. Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling has faced several limitations for large models; however, recent advances in parallel computing and advanced proposal schemes have opened routes less traveled. This paper presents Bayesian autoencoders powered by MCMC sampling implemented using parallel computing and Langevin-gradient proposal distribution. The results indicate that the proposed Bayesian autoencoder provides similar performance accuracy when compared to related methods in the literature. Furthermore, it provides uncertainty quantification in the reduced data representation. This motivates further applications of the Bayesian autoencoder framework for other deep learning models.

The work of McCloskey and Cohen popularized the concept of catastrophic interference. They used a neural network that tried to learn addition using two groups of examples as two different tasks. In their case, learning the second task rapidly deteriorated the acquired knowledge about the previous one. We hypothesize that this could be a symptom of a fundamental problem: addition is an algorithmic task that should not be learned through pattern recognition. Therefore, other model architectures better suited for this task would avoid catastrophic forgetting. We use a neural network with a different architecture that can be trained to recover the correct algorithm for the addition of binary numbers. This neural network includes conditional clauses that are naturally treated within the back-propagation algorithm. We test it in the setting proposed by McCloskey and Cohen and training on random additions one by one. The neural network not only does not suffer from catastrophic forgetting but it improves its predictive power on unseen pairs of numbers as training progresses. We also show that this is a robust effect, also present when averaging many simulations. This work emphasizes the importance that neural network architecture has for the emergence of catastrophic forgetting and introduces a neural network that is able to learn an algorithm.

Ultrasound shear wave elasticity imaging is a valuable tool for quantifying the elastic properties of tissue. Typically, the shear wave velocity is derived and mapped to an elasticity value, which neglects information such as the shape of the propagating shear wave or push sequence characteristics. We present 3D spatio-temporal CNNs for fast local elasticity estimation from ultrasound data. This approach is based on retrieving elastic properties from shear wave propagation within small local regions. A large training data set is acquired with a robot from homogeneous gelatin phantoms ranging from 17.42 kPa to 126.05 kPa with various push locations. The results show that our approach can estimate elastic properties on a pixelwise basis with a mean absolute error of 5.01+-4.37 kPa. Furthermore, we estimate local elasticity independent of the push location and can even perform accurate estimates inside the push region. For phantoms with embedded inclusions, we report a 53.93% lower MAE (7.50 kPa) and on the background of 85.24% (1.64 kPa) compared to a conventional shear wave method. Overall, our method offers fast local estimations of elastic properties with small spatio-temporal window sizes.

Cross-modal encoders for vision-language (VL) tasks are often pretrained with carefully curated vision-language datasets. While these datasets reach an order of 10 million samples, the labor cost is prohibitive to scale further. Conversely, unimodal encoders are pretrained with simpler annotations that are less cost-prohibitive, achieving scales of hundreds of millions to billions. As a result, unimodal encoders have achieved state-of-art (SOTA) on many downstream tasks. However, challenges remain when applying to VL tasks. The pretraining data is not optimal for cross-modal architectures and requires heavy computational resources. In addition, unimodal architectures lack cross-modal interactions that have demonstrated significant benefits for VL tasks. Therefore, how to best leverage pretrained unimodal encoders for VL tasks is still an area of active research. In this work, we propose a method to leverage unimodal vision and text encoders for VL tasks that augment existing VL approaches while conserving computational complexity. Specifically, we propose Multimodal Adaptive Distillation (MAD), which adaptively distills useful knowledge from pretrained encoders to cross-modal VL encoders. Second, to better capture nuanced impacts on VL task performance, we introduce an evaluation protocol that includes Visual Commonsense Reasoning (VCR), Visual Entailment (SNLI-VE), and Visual Question Answering (VQA), across a variety of data constraints and conditions of domain shift. Experiments demonstrate that MAD leads to consistent gains in the low-shot, domain-shifted, and fully-supervised conditions on VCR, SNLI-VE, and VQA, achieving SOTA performance on VCR compared to other single models pretrained with image-text data. Finally, MAD outperforms concurrent works utilizing pretrained vision encoder from CLIP. Code will be made available.

Recent years have witnessed the great success of multi-agent systems (MAS). Value decomposition, which decomposes joint action values into individual action values, has been an important work in MAS. However, many value decomposition methods ignore the coordination among different agents, leading to the notorious "lazy agents" problem. To enhance the coordination in MAS, this paper proposes HyperGraph CoNvolution MIX (HGCN-MIX), a method that incorporates hypergraph convolution with value decomposition. HGCN-MIX models agents as well as their relationships as a hypergraph, where agents are nodes and hyperedges among nodes indicate that the corresponding agents can coordinate to achieve larger rewards. Then, it trains a hypergraph that can capture the collaborative relationships among agents. Leveraging the learned hypergraph to consider how other agents' observations and actions affect their decisions, the agents in a MAS can better coordinate. We evaluate HGCN-MIX in the StarCraft II multi-agent challenge benchmark. The experimental results demonstrate that HGCN-MIX can train joint policies that outperform or achieve a similar level of performance as the current state-of-the-art techniques. We also observe that HGCN-MIX has an even more significant improvement of performance in the scenarios with a large amount of agents. Besides, we conduct additional analysis to emphasize that when the hypergraph learns more relationships, HGCN-MIX can train stronger joint policies.

Prediction of a machine's Remaining Useful Life (RUL) is one of the key tasks in predictive maintenance. The task is treated as a regression problem where Machine Learning (ML) algorithms are used to predict the RUL of machine components. These ML algorithms are generally used as a black box with a total focus on the performance without identifying the potential causes behind the algorithms' decisions and their working mechanism. We believe, the performance (in terms of Mean Squared Error (MSE), etc.,) alone is not enough to build the trust of the stakeholders in ML prediction rather more insights on the causes behind the predictions are needed. To this aim, in this paper, we explore the potential of Explainable AI (XAI) techniques by proposing an explainable regression framework for the prediction of machines' RUL. We also evaluate several ML algorithms including classical and Neural Networks (NNs) based solutions for the task. For the explanations, we rely on two model agnostic XAI methods namely Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). We believe, this work will provide a baseline for future research in the domain.

The growing size of neural language models has led to increased attention in model compression. The two predominant approaches are pruning, which gradually removes weights from a pre-trained model, and distillation, which trains a smaller compact model to match a larger one. Pruning methods can significantly reduce the model size but hardly achieve large speedups as distillation. However, distillation methods require large amounts of unlabeled data and are expensive to train. In this work, we propose a task-specific structured pruning method CoFi (Coarse- and Fine-grained Pruning), which delivers highly parallelizable subnetworks and matches the distillation methods in both accuracy and latency, without resorting to any unlabeled data. Our key insight is to jointly prune coarse-grained (e.g., layers) and fine-grained (e.g., heads and hidden units) modules, which controls the pruning decision of each parameter with masks of different granularity. We also devise a layerwise distillation strategy to transfer knowledge from unpruned to pruned models during optimization. Our experiments on GLUE and SQuAD datasets show that CoFi yields models with over 10x speedups with a small accuracy drop, showing its effectiveness and efficiency compared to previous pruning and distillation approaches.

Purpose: To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated Magnetic Resonance Imaging (MRI) data. Methods: Scan-Specific Artifact Reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to GRAPPA and demonstrates improved robustness over other scan-specific models, such as RAKI and residual-RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave-encoded images. Results: SPARK yields 1.5x - 2x RMSE reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan-specific techniques. When applied to advanced reconstruction techniques such as residual-RAKI, 2D VC-GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves performance by ~2x and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non-cartesian 2D and 3D wave-encoding imaging by reducing RMSE between 20-25% and providing qualitative improvements. Conclusion: SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.

Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods under realistic conditions is difficult. Existing work therefore often draws on different heuristics and simulations that do not necessarily produce meaningful and reliable results. Indeed, in the absence of common evaluation standards, it often remains unclear how online learning methods will perform in practice or in comparison to similar work. In this paper, we propose a comprehensive set of properties for high-quality machine learning in evolving data streams. In particular, we discuss sensible performance measures and evaluation strategies for online predictive modelling, online feature selection and concept drift detection. As one of the first works, we also look at the interpretability of online learning methods. The proposed evaluation standards are provided in a new Python framework called float. Float is completely modular and allows the simultaneous integration of common libraries, such as scikit-multiflow or river, with custom code. Float is open-sourced and can be accessed at https://github.com/haugjo/float. In this sense, we hope that our work will contribute to more standardized, reliable and realistic testing and comparison of online machine learning methods.

Power sector capacity expansion models (CEMs) that are used for studying future low-carbon grid scenarios must incorporate detailed representation of grid operations. Often CEMs are formulated to model grid operations over representative periods that are sampled from the original input data using clustering algorithms. However, such representative period selection (RPS) methods are limited by the declining efficacy of the clustering algorithm with increasing dimensionality of the input data and do not consider the relative importance of input data variations on CEM outcomes. Here, we propose a RPS method that addresses these limitations by incorporating dimensionality reduction, accomplished via neural network based autoencoders, prior to clustering. Such dimensionality reduction not only improves the performance of the clustering algorithm, but also facilitates using additional features, such as estimated outputs produced from parallel solutions of simplified versions of the CEM for each disjoint period in the input data (e.g. 1 week). The impact of incorporating dimensionality reduction as part of RPS methods is quantified through the error in outcomes of the corresponding reduced-space CEM vs. the full space CEM. Extensive numerical experimentation across various networks and range of technology and policy scenarios establish the superiority of the dimensionality-reduction based RPS methods.

Compositional generalization is a critical ability in learning and decision-making. We focus on the setting of reinforcement learning in object-oriented environments to study compositional generalization in world modeling. We (1) formalize the compositional generalization problem with an algebraic approach and (2) study how a world model can achieve that. We introduce a conceptual environment, Object Library, and two instances, and deploy a principled pipeline to measure the generalization ability. Motivated by the formulation, we analyze several methods with exact} or no compositional generalization ability using our framework, and design a differentiable approach, Homomorphic Object-oriented World Model (HOWM), that achieves approximate but more efficient compositional generalization.

Scalability is an important consideration for deep graph neural networks. Inspired by the conventional pooling layers in CNNs, many recent graph learning approaches have introduced the pooling strategy to reduce the size of graphs for learning, such that the scalability and efficiency can be improved. However, these pooling-based methods are mainly tailored to a single graph-level task and pay more attention to local information, limiting their performance in multi-task settings which often require task-specific global information. In this paper, departure from these pooling-based efforts, we design a new approach called DOTIN (\underline{D}r\underline{o}pping \underline{T}ask-\underline{I}rrelevant \underline{N}odes) to reduce the size of graphs. Specifically, by introducing $K$ learnable virtual nodes to represent the graph embeddings targeted to $K$ different graph-level tasks, respectively, up to 90\% raw nodes with low attentiveness with an attention model -- a transformer in this paper, can be adaptively dropped without notable performance decreasing. Achieving almost the same accuracy, our method speeds up GAT by about 50\% on graph-level tasks including graph classification and graph edit distance (GED) with about 60\% less memory, on D\&D dataset. Code will be made publicly available in https://github.com/Sherrylone/DOTIN.

Specialized documentation techniques have been developed to communicate key facts about machine-learning (ML) systems and the datasets and models they rely on. Techniques such as Datasheets, FactSheets, and Model Cards have taken a mainly descriptive approach, providing various details about the system components. While the above information is essential for product developers and external experts to assess whether the ML system meets their requirements, other stakeholders might find it less actionable. In particular, ML engineers need guidance on how to mitigate potential shortcomings in order to fix bugs or improve the system's performance. We survey approaches that aim to provide such guidance in a prescriptive way. We further propose a preliminary approach, called Method Cards, which aims to increase the transparency and reproducibility of ML systems by providing prescriptive documentation of commonly-used ML methods and techniques. We showcase our proposal with an example in small object detection, and demonstrate how Method Cards can communicate key considerations for model developers. We further highlight avenues for improving the user experience of ML engineers based on Method Cards.

Federated learning (FL) provides a privacy-preserving solution for distributed machine learning tasks. One challenging problem that severely damages the performance of FL models is the co-occurrence of data heterogeneity and long-tail distribution, which frequently appears in real FL applications. In this paper, we reveal an intriguing fact that the biased classifier is the primary factor leading to the poor performance of the global model. Motivated by the above finding, we propose a novel and privacy-preserving FL method for heterogeneous and long-tailed data via Classifier Re-training with Federated Features (CReFF). The classifier re-trained on federated features can produce comparable performance as the one re-trained on real data in a privacy-preserving manner without information leakage of local data or class distribution. Experiments on several benchmark datasets show that the proposed CReFF is an effective solution to obtain a promising FL model under heterogeneous and long-tailed data. Comparative results with the state-of-the-art FL methods also validate the superiority of CReFF. Our code is available at https://github.com/shangxinyi/CReFF-FL.

Worst-case dynamic PDN noise analysis is an essential step in PDN sign-off to ensure the performance and reliability of chips. However, with the growing PDN size and increasing scenarios to be validated, it becomes very time- and resource-consuming to conduct full-stack PDN simulation to check the worst-case noise for different test vectors. Recently, various works have proposed machine learning based methods for supply noise prediction, many of which still suffer from large training overhead, inefficiency, or non-scalability. Thus, this paper proposed an efficient and scalable framework for the worst-case dynamic PDN noise prediction. The framework first reduces the spatial and temporal redundancy in the PDN and input current vector, and then employs efficient feature extraction as well as a novel convolutional neural network architecture to predict the worst-case dynamic PDN noise. Experimental results show that the proposed framework consistently outperforms the commercial tool and the state-of-the-art machine learning method with only 0.63-1.02% mean relative error and 25-69$\times$ speedup.

Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In this paper, we present a hierarchical multi-label representation learning framework that can leverage all available labels and preserve the hierarchical relationship between classes. We introduce novel hierarchy preserving losses, which jointly apply a hierarchical penalty to the contrastive loss, and enforce the hierarchy constraint. The loss function is data driven and automatically adapts to arbitrary multi-label structures. Experiments on several datasets show that our relationship-preserving embedding performs well on a variety of tasks and outperform the baseline supervised and self-supervised approaches. Code is available at https://github.com/salesforce/hierarchicalContrastiveLearning.

We consider the design of reliable neural network (NN)-based approximations of traditional stabilizing controllers for linear systems affected by polytopic uncertainty, including controllers with variable structure and those based on a minimal selection policy. We develop a systematic procedure to certify the closed-loop stability and performance of a polytopic system when a rectified linear unit (ReLU)-based approximation replaces such traditional controllers. We provide sufficient conditions to ensure stability involving the worst-case approximation error and the Lipschitz constant characterizing the error function between ReLU-based and traditional controller-based state-to-input mappings, and further provide offline, mixed-integer optimization-based methods that allow us to compute those quantities exactly.

Epilepsy affects 50 million people worldwide and is one of the most common serious neurological disorders. Seizure detection and classification is a valuable tool for diagnosing and maintaining the condition. An automated classification algorithm will allow for accurate diagnosis. Utilising the Temple University Hospital (TUH) Seizure Corpus, six seizure types are compared; absence, complex partial, myoclonic, simple partial, tonic and tonic- clonic models. This study proposes a method that utilises unique features with a novel parallel classifier - Parallel Genetic Naive Bayes (NB) Seizure Classifier (PGNBSC). The PGNBSC algorithm searches through the features and by reclassifying the data each time, the algorithm will create a matrix for optimum search criteria. Ictal states from the EEGs are segmented into 1.8 s windows, where the epochs are then further decomposed into 13 different features from the first intrinsic mode function (IMF). The features are compared using an original NB classifier in the first model. This is improved upon in a second model by using a genetic algorithm (Binary Grey Wolf Optimisation, Option 1) with a NB classifier. The third model uses a combination of the simple partial and complex partial seizures to provide the highest classification accuracy for each of the six seizures amongst the three models (20%, 53%, and 85% for first, second, and third model, respectively).

Federated learning is growing fast in both academia and industry to resolve data hungriness and privacy issues in machine learning. A federated learning system being widely distributed with different components and stakeholders requires software system design thinking. For instance, multiple patterns and tactics have been summarised by researchers that cover various aspects, from client management, training configuration, model deployment, etc. However, the multitude of patterns leaves the designers confused about when and which pattern to adopt or adapt. Therefore, in this paper, we present a set of decision models to assist designers and architects who have limited knowledge in federated learning, in selecting architectural patterns for federated learning architecture design. Each decision model maps functional and non-functional requirements of federated learning systems to a set of patterns. we also clarify the trade-offs that may be implicit in the patterns. We evaluated the decision model through a set of interviews with practitioners to assess the correctness and usefulness in guiding the architecture design process through various design decision options.

Hierarchical VAEs have emerged in recent years as a reliable option for maximum likelihood estimation. However, instability issues and demanding computational requirements have hindered research progress in the area. We present simple modifications to the Very Deep VAE to make it converge up to $2.6\times$ faster, save up to $20\times$ in memory load and improve stability during training. Despite these changes, our models achieve comparable or better negative log-likelihood performance than current state-of-the-art models on all $7$ commonly used image datasets we evaluated on. We also make an argument against using 5-bit benchmarks as a way to measure hierarchical VAE's performance due to undesirable biases caused by the 5-bit quantization. Additionally, we empirically demonstrate that roughly $3\%$ of the hierarchical VAE's latent space dimensions is sufficient to encode most of the image information, without loss of performance, opening up the doors to efficiently leverage the hierarchical VAEs' latent space in downstream tasks. We release our source code and models at https://github.com/Rayhane-mamah/Efficient-VDVAE .

A long time ago in the machine learning literature, the idea of incorporating a mechanism inspired by the human visual system into neural networks was introduced. This idea is named the attention mechanism, and it has gone through a long development period. Today, many works have been devoted to this idea in a variety of tasks. Remarkable performance has recently been demonstrated. The goal of this paper is to provide an overview from the early work on searching for ways to implement attention idea with neural networks until the recent trends. This review emphasizes the important milestones during this progress regarding different tasks. By this way, this study aims to provide a road map for researchers to explore the current development and get inspired for novel approaches beyond the attention.

This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods. Traditional heuristic solutions like Weighted-Cost Multi-Path (WCMP) and Local Shortest Queue (LSQ) are less flexible to the changing workload distributions and arrival rates, with a poor balance among multiple load balancers. The cooperative network load balancing task is formulated as a Dec-POMDP problem, which naturally induces the MARL methods. To bridge the reality gap for applying learning-based methods, all methods are directly trained and evaluated on an emulation system from moderate-to large-scale. Experiments on realistic testbeds show that the independent and "selfish" load balancing strategies are not necessarily the globally optimal ones, while the proposed MARL solution has a superior performance over different realistic settings. Additionally, the potential difficulties of MARL methods for network load balancing are analysed, which helps to draw the attention of the learning and network communities to such challenges.

Professional news media organizations have always touted the importance that they give to multiple perspectives. However, in practice the traditional approach to all-sides has favored people in the dominant culture. Hence it has come under ethical critique under the new norms of diversity, equity, and inclusion (DEI). When DEI is applied to journalism, it goes beyond conventional notions of impartiality and bias and instead democratizes the journalistic practice of sourcing -- who is quoted or interviewed, who is not, how often, from which demographic group, gender, and so forth. There is currently no real-time or on-demand tool in the hands of reporters to analyze the persons they quote. In this paper, we present DIANES, a DEI Audit Toolkit for News Sources. It consists of a natural language processing pipeline on the backend to extract quotes, speakers, titles, and organizations from news articles in real time. On the frontend, DIANES offers the WordPress plugins, a Web monitor, and a DEI annotation API service, to help news media monitor their own quoting patterns and push themselves towards DEI norms.

Machine-learning models are ubiquitous. In some domains, for instance, in medicine, the models' predictions must be interpretable. Decision trees, classification rules, and subgroup discovery are three broad categories of supervised machine-learning models presenting knowledge in the form of interpretable rules. The accuracy of these models learned from small datasets is usually low. Obtaining larger datasets is often hard to impossible. Pedagogical rule extraction methods could help to learn better rules from small data by augmenting a dataset employing statistical models and using it to learn a rule-based model. However, existing evaluation of these methods is often inconclusive, and they were not compared so far. Our framework PRELIM unifies existing pedagogical rule extraction techniques. In the extensive experiments, we identified promising PRELIM configurations not studied before.

In this paper, a novel multimode dynamic process monitoring approach is proposed by extending elastic weight consolidation (EWC) to probabilistic slow feature analysis (PSFA) in order to extract multimode slow features for online monitoring. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode dynamic process monitoring. When a new mode arrives, a set of data should be collected so that this mode can be identified by PSFA and prior knowledge. Then, a regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is denoted as PSFA-EWC, which is updated continually and capable of achieving excellent performance for successive modes. Different from traditional multimode monitoring algorithms, PSFA-EWC furnishes backward and forward transfer ability. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. Compared with several known methods, the effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system.

We study iterative methods based on Krylov subspaces for low-rank approximation under any Schatten-$p$ norm. Here, given access to a matrix $A$ through matrix-vector products, an accuracy parameter $\epsilon$, and a target rank $k$, the goal is to find a rank-$k$ matrix $Z$ with orthonormal columns such that $\| A(I -ZZ^\top)\|_{S_p} \leq (1+\epsilon)\min_{U^\top U = I_k} \|A(I - U U^\top)\|_{S_p}$, where $\|M\|_{S_p}$ denotes the $\ell_p$ norm of the the singular values of $M$. For the special cases of $p=2$ (Frobenius norm) and $p = \infty$ (Spectral norm), Musco and Musco (NeurIPS 2015) obtained an algorithm based on Krylov methods that uses $\tilde{O}(k/\sqrt{\epsilon})$ matrix-vector products, improving on the na\"ive $\tilde{O}(k/\epsilon)$ dependence obtainable by the power method, where $\tilde{O}$ suppresses poly$(\log(dk/\epsilon))$ factors. Our main result is an algorithm that uses only $\tilde{O}(kp^{1/6}/\epsilon^{1/3})$ matrix-vector products, and works for all $p \geq 1$. For $p = 2$ our bound improves the previous $\tilde{O}(k/\epsilon^{1/2})$ bound to $\tilde{O}(k/\epsilon^{1/3})$. Since the Schatten-$p$ and Schatten-$\infty$ norms are the same up to a $1+ \epsilon$ factor when $p \geq (\log d)/\epsilon$, our bound recovers the result of Musco and Musco for $p = \infty$. Further, we prove a matrix-vector query lower bound of $\Omega(1/\epsilon^{1/3})$ for any fixed constant $p \geq 1$, showing that surprisingly $\tilde{\Theta}(1/\epsilon^{1/3})$ is the optimal complexity for constant~$k$. To obtain our results, we introduce several new techniques, including optimizing over multiple Krylov subspaces simultaneously, and pinching inequalities for partitioned operators. Our lower bound for $p \in [1,2]$ uses the Araki-Lieb-Thirring trace inequality, whereas for $p>2$, we appeal to a norm-compression inequality for aligned partitioned operators.

An increasing number of communication and computational schemes with quantum advantages have recently been proposed, which implies that quantum technology has fertile application prospects. However, demonstrating these schemes experimentally continues to be a central challenge because of the difficulty in preparing high-dimensional states or highly entangled states. In this study, we introduce and analyse a quantum coupon collector protocol by employing coherent states and simple linear optical elements, which was successfully demonstrated using realistic experimental equipment. We showed that our protocol can significantly reduce the number of samples needed to learn a specific set compared with the classical limit of the coupon collector problem. We also discuss the potential values and expansions of the quantum coupon collector by constructing a quantum blind box game. The information transmitted by the proposed game also broke the classical limit. These results strongly prove the advantages of quantum mechanics in machine learning and communication complexity.

Adam and AdaBelief compute and make use of elementwise adaptive stepsizes in training deep neural networks (DNNs) by tracking the exponential moving average (EMA) of the squared-gradient g_t^2 and the squared prediction error (m_t-g_t)^2, respectively, where m_t is the first momentum at iteration t and can be viewed as a prediction of g_t. In this work, we investigate if layerwise gradient statistics can be expoited in Adam and AdaBelief to allow for more effective training of DNNs. We address the above research question in two steps. Firstly, we slightly modify Adam and AdaBelief by introducing layerwise adaptive stepsizes in their update procedures via either pre- or post-processing. Our empirical results indicate that the slight modification produces comparable performance for training VGG and ResNet models over CIFAR10 and CIFAR100, suggesting that layer-wise gradient statistics play an important role towards the success of Adam and AdaBelief for at least certian DNN tasks. In the second step, we propose Aida, a new optimisation method, with the objective that the elementwise stepsizes within each layer have significantly smaller statistical variances, and the layerwise average stepsizes are much more compact across all the layers. Motivated by the fact that (m_t-g_t)^2 in AdaBelief is conservative in comparison to g_t^2 in Adam in terms of layerwise statistical averages and variances, Aida is designed by tracking a more conservative function of m_t and g_t than (m_t-g_t)^2 via layerwise vector projections. Experimental results show that Aida produces either competitive or better performance with respect to a number of existing methods including Adam and AdaBelief for a set of challenging DNN tasks.

In this work we present a novel approach to hierarchical reinforcement learning for linearly-solvable Markov decision processes. Our approach assumes that the state space is partitioned, and the subtasks consist in moving between the partitions. We represent value functions on several levels of abstraction, and use the compositionality of subtasks to estimate the optimal values of the states in each partition. The policy is implicitly defined on these optimal value estimates, rather than being decomposed among the subtasks. As a consequence, our approach can learn the globally optimal policy, and does not suffer from the non-stationarity of high-level decisions. If several partitions have equivalent dynamics, the subtasks of those partitions can be shared. If the set of boundary states is smaller than the entire state space, our approach can have significantly smaller sample complexity than that of a flat learner, and we validate this empirically in several experiments.

Nonnegative matrix factorization (NMF) is the problem of approximating an input nonnegative matrix, $V$, as the product of two smaller nonnegative matrices, $W$ and $H$. In this paper, we introduce a general framework to design multiplicative updates (MU) for NMF based on $\beta$-divergences ($\beta$-NMF) with disjoint equality constraints, and with penalty terms in the objective function. By disjoint, we mean that each variable appears in at most one equality constraint. Our MU satisfy the set of constraints after each update of the variables during the optimization process, while guaranteeing that the objective function decreases monotonically. We showcase this framework on three NMF models, and show that it competes favorably the state of the art: (1)~$\beta$-NMF with sum-to-one constraints on the columns of $H$, (2) minimum-volume $\beta$-NMF with sum-to-one constraints on the columns of $W$, and (3) sparse $\beta$-NMF with $\ell_2$-norm constraints on the columns of $W$.

Unsupervised generation of high-quality multi-view-consistent images and 3D shapes using only collections of single-view 2D photographs has been a long-standing challenge. Existing 3D GANs are either compute-intensive or make approximations that are not 3D-consistent; the former limits quality and resolution of the generated images and the latter adversely affects multi-view consistency and shape quality. In this work, we improve the computational efficiency and image quality of 3D GANs without overly relying on these approximations. We introduce an expressive hybrid explicit-implicit network architecture that, together with other design choices, synthesizes not only high-resolution multi-view-consistent images in real time but also produces high-quality 3D geometry. By decoupling feature generation and neural rendering, our framework is able to leverage state-of-the-art 2D CNN generators, such as StyleGAN2, and inherit their efficiency and expressiveness. We demonstrate state-of-the-art 3D-aware synthesis with FFHQ and AFHQ Cats, among other experiments.

In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), making their evaluation challenging. Currently, the standard process for evaluating GGMs suffers from three critical limitations: i) it does not produce a single score which makes model selection challenging, ii) in many cases it fails to consider underlying edge and node features, and iii) it is prohibitively slow to perform. In this work, we mitigate these issues by searching for scalar, domain-agnostic, and scalable metrics for evaluating and ranking GGMs. To this end, we study existing GGM metrics and neural-network-based metrics emerging from generative models of images that use embeddings extracted from a task-specific network. Motivated by the power of certain Graph Neural Networks (GNNs) to extract meaningful graph representations without any training, we introduce several metrics based on the features extracted by an untrained random GNN. We design experiments to thoroughly test metrics on their ability to measure the diversity and fidelity of generated graphs, as well as their sample and computational efficiency. Depending on the quantity of samples, we recommend one of two random-GNN-based metrics that we show to be more expressive than pre-existing metrics. While we focus on applying these metrics to GGM evaluation, in practice this enables the ability to easily compute the dissimilarity between any two sets of graphs regardless of domain. Our code is released at: https://github.com/uoguelph-mlrg/GGM-metrics.

"The power of a generalization system follows directly from its biases" (Mitchell 1980). Today, CNNs are incredibly powerful generalisation systems -- but to what degree have we understood how their inductive bias influences model decisions? We here attempt to disentangle the various aspects that determine how a model decides. In particular, we ask: what makes one model decide differently from another? In a meticulously controlled setting, we find that (1.) irrespective of the network architecture or objective (e.g. self-supervised, semi-supervised, vision transformers, recurrent models) all models end up with a similar decision boundary. (2.) To understand these findings, we analysed model decisions on the ImageNet validation set from epoch to epoch and image by image. We find that the ImageNet validation set, among others, suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46.0% "trivial" and 11.5% "impossible" images (beyond label errors). Only 42.5% of the images could possibly be responsible for the differences between two models' decision boundaries. (3.) Only removing the "impossible" and "trivial" images allows us to see pronounced differences between models. (4.) Humans are highly accurate at predicting which images are "trivial" and "impossible" for CNNs (81.4%). This implies that in future comparisons of brains, machines and behaviour, much may be gained from investigating the decisive role of images and the distribution of their difficulties.

Fast inference of numerical model parameters from data is an important prerequisite to generate predictive models for a wide range of applications. Use of sampling-based approaches such as Markov chain Monte Carlo may become intractable when each likelihood evaluation is computationally expensive. New approaches combining variational inference with normalizing flow are characterized by a computational cost that grows only linearly with the dimensionality of the latent variable space, and rely on gradient-based optimization instead of sampling, providing a more efficient approach for Bayesian inference about the model parameters. Moreover, the cost of frequently evaluating an expensive likelihood can be mitigated by replacing the true model with an offline trained surrogate model, such as neural networks. However, this approach might generate significant bias when the surrogate is insufficiently accurate around the posterior modes. To reduce the computational cost without sacrificing inferential accuracy, we propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and surrogate model parameters. We also propose an efficient sample weighting scheme for surrogate model training that preserves global accuracy while effectively capturing high posterior density regions. We demonstrate the inferential and computational superiority of NoFAS against various benchmarks, including cases where the underlying model lacks identifiability. The source code and numerical experiments used for this study are available at https://github.com/cedricwangyu/NoFAS.

We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithm works with any underlying model and (unknown) data-generating distribution and does not require model refitting. The framework addresses, among other examples, false discovery rate control in multi-label classification, intersection-over-union control in instance segmentation, and the simultaneous control of the type-1 error of outlier detection and confidence set coverage in classification or regression. Our main insight is to reframe the risk-control problem as multiple hypothesis testing, enabling techniques and mathematical arguments different from those in the previous literature. We use our framework to provide new calibration methods for several core machine learning tasks with detailed worked examples in computer vision and tabular medical data.

Inspired by the diversity of biological neurons, quadratic artificial neurons can play an important role in deep learning models. The type of quadratic neurons of our interest replaces the inner-product operation in the conventional neuron with a quadratic function. Despite promising results so far achieved by networks of quadratic neurons, there are important issues not well addressed. Theoretically, the superior expressivity of a quadratic network over either a conventional network or a conventional network via quadratic activation is not fully elucidated, which makes the use of quadratic networks not well grounded. Practically, although a quadratic network can be trained via generic backpropagation, it can be subject to a higher risk of collapse than the conventional counterpart. To address these issues, we first apply the spline theory and a measure from algebraic geometry to give two theorems that demonstrate better model expressivity of a quadratic network than the conventional counterpart with or without quadratic activation. Then, we propose an effective and efficient training strategy referred to as ReLinear to stabilize the training process of a quadratic network, thereby unleashing the full potential in its associated machine learning tasks. Comprehensive experiments on popular datasets are performed to support our findings and evaluate the performance of quadratic deep learning.

The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, product reviews, and even abusive content. Despite the prominent performance of existing methods for artificial text detection, they still lack interpretability and robustness towards unseen models. To this end, we propose three novel types of interpretable topological features for this task based on Topological Data Analysis (TDA) which is currently understudied in the field of NLP. We empirically show that the features derived from the BERT model outperform count- and neural-based baselines up to 10\% on three common datasets, and tend to be the most robust towards unseen GPT-style generation models as opposed to existing methods. The probing analysis of the features reveals their sensitivity to the surface and syntactic properties. The results demonstrate that TDA is a promising line with respect to NLP tasks, specifically the ones that incorporate surface and structural information.

Small and cluttered objects are common in real-world which are challenging for detection. The difficulty is further pronounced when the objects are rotated, as traditional detectors often routinely locate the objects in horizontal bounding box such that the region of interest is contaminated with background or nearby interleaved objects. In this paper, we first innovatively introduce the idea of denoising to object detection. Instance-level denoising on the feature map is performed to enhance the detection to small and cluttered objects. To handle the rotation variation, we also add a novel IoU constant factor to the smooth L1 loss to address the long standing boundary problem, which to our analysis, is mainly caused by the periodicity of angular (PoA) and exchangeability of edges (EoE). By combing these two features, our proposed detector is termed as SCRDet++. Extensive experiments are performed on large aerial images public datasets DOTA, DIOR, UCAS-AOD as well as natural image dataset COCO, scene text dataset ICDAR2015, small traffic light dataset BSTLD and our released S$^2$TLD by this paper. The results show the effectiveness of our approach. The released dataset S2TLD is made public available, which contains 5,786 images with 14,130 traffic light instances across five categories.

This study proposes a new generative adversarial network (GAN) for generating realistic orders in financial markets. In some previous works, GANs for financial markets generated fake orders in continuous spaces because of GAN architectures' learning limitations. However, in reality, the orders are discrete, such as order prices, which has minimum order price unit, or order types. Thus, we change the generation method to place the generated fake orders into discrete spaces in this study. Because this change disabled the ordinary GAN learning algorithm, this study employed a policy gradient, frequently used in reinforcement learning, for the learning algorithm. Through our experiments, we show that our proposed model outperforms previous models in generated order distribution. As an additional benefit of introducing the policy gradient, the entropy of the generated policy can be used to check GAN's learning status. In the future, higher performance GANs, better evaluation methods, or the applications of our GANs can be addressed.

DNA-encoded library (DEL) screening and quantitative structure-activity relationship (QSAR) modeling are two techniques used in drug discovery to find small molecules that bind a protein target. Applying QSAR modeling to DEL data can facilitate the selection of compounds for off-DNA synthesis and evaluation. Such a combined approach has been shown recently by training binary classifiers to learn DEL enrichments of aggregated "disynthons" to accommodate the sparse and noisy nature of DEL data. However, a binary classifier cannot distinguish between different levels of enrichment, and information is potentially lost during disynthon aggregation. Here, we demonstrate a regression approach to learning DEL enrichments of individual molecules using a custom negative log-likelihood loss function that effectively denoises DEL data and introduces opportunities for visualization of learned structure-activity relationships (SAR). Our approach explicitly models the Poisson statistics of the sequencing process used in the DEL experimental workflow under a frequentist view. We illustrate this approach on a dataset of 108k compounds screened against CAIX, and a dataset of 5.7M compounds screened against sEH and SIRT2. Due to the treatment of uncertainty in the data through the negative log-likelihood loss function, the models can ignore low-confidence outliers. While our approach does not demonstrate a benefit for extrapolation to novel structures, we expect our denoising and visualization pipeline to be useful in identifying SAR trends and enriched pharmacophores in DEL data. Further, this approach to uncertainty-aware regression is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.

We propose a unified multi-tasking framework to represent the complex and uncertain causal process of financial market dynamics, and then to predict the movement of any type of index with an application on the monthly direction of the S&P500 index. our solution is based on three main pillars: (i) the use of transfer learning to share knowledge and feature (representation, learning) between all financial markets, increase the size of the training sample and preserve the stability between training, validation and test sample. (ii) The combination of multidisciplinary knowledge (Financial economics, behavioral finance, market microstructure and portfolio construction theories) to represent a global top-down dynamics of any financial market, through a graph. (iii) The integration of forward looking unstructured data, different types of contexts (long, medium and short term) through latent variables/nodes and then, use a unique VAE network (parameter sharing) to learn simultaneously their distributional representation. We obtain Accuracy, F1-score, and Matthew Correlation of 74.3 %, 67 % and 0.42 above the industry and other benchmark on 12 years test period which include three unstable and difficult sub-period to predict.

The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function. When learning to achieve multiple goals, data efficiency is intimately connected with the generalization of the Q-function to new goals. The de-facto paradigm is to approximate Q(s, a, g) using monolithic neural networks. To improve the generalization of the Q-function, we propose a bilinear decomposition that represents the Q-value via a low-rank approximation in the form of a dot product between two vector fields. The first vector field, f(s, a), captures the environment's local dynamics at the state s; whereas the second component, {\phi}(s, g), captures the global relationship between the current state and the goal. We show that our bilinear decomposition scheme substantially improves data efficiency, and has superior transfer to out-of-distribution goals compared to prior methods. Empirical evidence is provided on the simulated Fetch robot task-suite and dexterous manipulation with a Shadow hand.

Recently, the seminal algorithms AlphaGo and AlphaZero have started a new era in game learning and deep reinforcement learning. While the achievements of AlphaGo and AlphaZero - playing Go and other complex games at super human level - are truly impressive, these architectures have the drawback that they are very complex and require high computational resources. Many researchers are looking for methods that are similar to AlphaZero, but have lower computational demands and are thus more easily reproducible. In this paper, we pick an important element of AlphaZero - the Monte Carlo Tree Search (MCTS) planning stage - and combine it with reinforcement learning (RL) agents. We wrap MCTS for the first time around RL n-tuple networks to create versatile agents that keep at the same time the computational demands low. We apply this new architecture to several complex games (Othello, ConnectFour, Rubik's Cube) and show the advantages achieved with this AlphaZero-inspired MCTS wrapper. In particular, we present results that this AlphaZero-inspired agent is the first one trained on standard hardware (no GPU or TPU) to beat the very strong Othello program Edax up to and including level 7 (where most other algorithms could only defeat Edax up to level 2).

Sum-Product Networks (SPNs) are expressive probabilistic models that provide exact, tractable inference. They achieve this efficiency by making use of local independence. On the other hand, mixtures of exchangeable variable models (MEVMs) are a class of tractable probabilistic models that make use of exchangeability of discrete random variables to render inference tractable. Exchangeability, which arises naturally in relational domains, has not been considered for efficient representation and inference in SPNs yet. The contribution of this paper is a novel probabilistic model which we call Exchangeability-Aware Sum-Product Networks (XSPNs). It contains both SPNs and MEVMs as special cases, and combines the ability of SPNs to efficiently learn deep probabilistic models with the ability of MEVMs to efficiently handle exchangeable random variables. We introduce a structure learning algorithm for XSPNs and empirically show that they can be more accurate than conventional SPNs when the data contains repeated, interchangeable parts.

In this working paper we present our current progress in the training of machine learning models to execute short option strategies on the S&P500. As a first step, this paper is breaking this problem down to a supervised classification task to decide if a short straddle on the S&P500 should be executed or not on a daily basis. We describe our used framework and present an overview over our evaluation metrics on different classification models. In this preliminary work, using standard machine learning techniques and without hyperparameter search, we find no statistically significant outperformance to a simple "trade always" strategy, but gain additional insights on how we could proceed in further experiments.

We consider distributed optimization under communication constraints for training deep learning models. We propose a new algorithm, whose parameter updates rely on two forces: a regular gradient step, and a corrective direction dictated by the currently best-performing worker (leader). Our method differs from the parameter-averaging scheme EASGD in a number of ways: (i) our objective formulation does not change the location of stationary points compared to the original optimization problem; (ii) we avoid convergence decelerations caused by pulling local workers descending to different local minima to each other (i.e. to the average of their parameters); (iii) our update by design breaks the curse of symmetry (the phenomenon of being trapped in poorly generalizing sub-optimal solutions in symmetric non-convex landscapes); and (iv) our approach is more communication efficient since it broadcasts only parameters of the leader rather than all workers. We provide theoretical analysis of the batch version of the proposed algorithm, which we call Leader Gradient Descent (LGD), and its stochastic variant (LSGD). Finally, we implement an asynchronous version of our algorithm and extend it to the multi-leader setting, where we form groups of workers, each represented by its own local leader (the best performer in a group), and update each worker with a corrective direction comprised of two attractive forces: one to the local, and one to the global leader (the best performer among all workers). The multi-leader setting is well-aligned with current hardware architecture, where local workers forming a group lie within a single computational node and different groups correspond to different nodes. For training convolutional neural networks, we empirically demonstrate that our approach compares favorably to state-of-the-art baselines. This work is a gentle extension of [2].

This paper studies a new design of the optimization algorithm for training deep learning models with a fixed architecture of the classification network in a continual learning framework. The training data is non-stationary and the non-stationarity is imposed by a sequence of distinct tasks. We first analyze a deep model trained on only one learning task in isolation and identify a region in network parameter space, where the model performance is close to the recovered optimum. We provide empirical evidence that this region resembles a cone that expands along the convergence direction. We study the principal directions of the trajectory of the optimizer after convergence and show that traveling along a few top principal directions can quickly bring the parameters outside the cone but this is not the case for the remaining directions. We argue that catastrophic forgetting in a continual learning setting can be alleviated when the parameters are constrained to stay within the intersection of the plausible cones of individual tasks that were so far encountered during training. Based on this observation we present our direction-constrained optimization (DCO) method, where for each task we introduce a linear autoencoder to approximate its corresponding top forbidden principal directions. They are then incorporated into the loss function in the form of a regularization term for the purpose of learning the coming tasks without forgetting. Furthermore, in order to control the memory growth as the number of tasks increases, we propose a memory-efficient version of our algorithm called compressed DCO (DCO-COMP) that allocates a memory of fixed size for storing all autoencoders. We empirically demonstrate that our algorithm performs favorably compared to other state-of-art regularization-based continual learning methods.

Recent works have demonstrated ability to assess aspects of mental health from personal discourse. At the same time, pre-trained contextual word embedding models have grown to dominate much of NLP but little is known empirically on how to best apply them for mental health assessment. Using degree of depression as a case study, we do an empirical analysis on which off-the-shelf language model, individual layers, and combinations of layers seem most promising when applied to human-level NLP tasks. Notably, we find RoBERTa most effective and, despite the standard in past work suggesting the second-to-last or concatenation of the last 4 layers, we find layer 19 (sixth-to last) is at least as good as layer 23 when using 1 layer. Further, when using multiple layers, distributing them across the second half (i.e. Layers 12+), rather than last 4, of the 24 layers yielded the most accurate results.

Malicious advertisement URLs pose a security risk since they are the source of cyber-attacks, and the need to address this issue is growing in both industry and academia. Generally, the attacker delivers an attack vector to the user by means of an email, an advertisement link or any other means of communication and directs them to a malicious website to steal sensitive information and to defraud them. Existing malicious URL detection techniques are limited and to handle unseen features as well as generalize to test data. In this study, we extract a novel set of lexical and web-scrapped features and employ machine learning technique to set up system for fraudulent advertisement URLs detection. The combination set of six different kinds of features precisely overcome the obfuscation in fraudulent URL classification. Based on different statistical properties, we use twelve different formatted datasets for detection, prediction and classification task. We extend our prediction analysis for mismatched and unlabelled datasets. For this framework, we analyze the performance of four machine learning techniques: Random Forest, Gradient Boost, XGBoost and AdaBoost in the detection part. With our proposed method, we can achieve a false negative rate as low as 0.0037 while maintaining high accuracy of 99.63%. Moreover, we devise a novel unsupervised technique for data clustering using K- Means algorithm for the visual analysis. This paper analyses the vulnerability of decision tree-based models using the limited knowledge attack scenario. We considered the exploratory attack and implemented Zeroth Order Optimization adversarial attack on the detection models.

Computer-aided medical image analysis plays a significant role in assisting medical practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present, convolutional neural networks (CNN) are the preferred choice for medical image analysis. In addition, with the rapid advancements in three-dimensional (3D) imaging systems and the availability of excellent hardware and software support to process large volumes of data, 3D deep learning methods are gaining popularity in medical image analysis. Here, we present an extensive review of the recently evolved 3D deep learning methods in medical image segmentation. Furthermore, the research gaps and future directions in 3D medical image segmentation are discussed.

With the ever increasing data deluge and the success of deep neural networks, the research of distributed deep learning has become pronounced. Two common approaches to achieve this distributed learning is synchronous and asynchronous weight update. In this manuscript, we have explored very simplistic synchronous weight update mechanisms. It has been seen that with an increasing number of worker nodes, the performance degrades drastically. This effect has been studied in the context of extreme imbalanced classification (e.g. outlier detection). In practical cases, the assumed conditions of i.i.d. may not be fulfilled. There may also arise global class imbalance situations like that of outlier detection where the local servers receive severely imbalanced data and may not get any samples from the minority class. In that case, the DNNs in the local servers will get completely biased towards the majority class that they receive. This would highly impact the learning at the parameter server (which practically does not see any data). It has been observed that in a parallel setting if one uses the existing federated weight update mechanisms at the parameter server, the performance degrades drastically with the increasing number of worker nodes. This is mainly because, with the increasing number of nodes, there is a high chance that one worker node gets a very small portion of the data, either not enough to train the model without overfitting or having a highly imbalanced class distribution. The chapter, hence, proposes a workaround to this problem by introducing the concept of adaptive cost-sensitive momentum averaging. It is seen that for the proposed system, there was no to minimal degradation in performance while most of the other methods hit their bottom performance before that.

We propose a method for finding approximate compilations of quantum unitary transformations, based on techniques from policy gradient reinforcement learning. The choice of a stochastic policy allows us to rephrase the optimization problem in terms of probability distributions, rather than variational gates. In this framework, finding the optimal configuration is done by optimizing over distribution parameters, rather than over free angles. We show numerically that this approach can be more competitive than gradient-free methods, for comparable amounts of resources (i.e. quantum circuit runs). Another interesting feature of this approach to variational compilation is that it does not need a separate register and long-range interactions to estimate the end-point fidelity, which is an improvement over methods which rely on the Hilbert-Schmidt test. We expect these techniques to be relevant for training variational circuits in other contexts.

Parametric computer-aided design (CAD) tools are the predominant way that engineers specify physical structures, from bicycle pedals to airplanes to printed circuit boards. The key characteristic of parametric CAD is that design intent is encoded not only via geometric primitives, but also by parameterized constraints between the elements. This relational specification can be viewed as the construction of a constraint program, allowing edits to coherently propagate to other parts of the design. Machine learning offers the intriguing possibility of accelerating the design process via generative modeling of these structures, enabling new tools such as autocompletion, constraint inference, and conditional synthesis. In this work, we present such an approach to generative modeling of parametric CAD sketches, which constitute the basic computational building blocks of modern mechanical design. Our model, trained on real-world designs from the SketchGraphs dataset, autoregressively synthesizes sketches as sequences of primitives, with initial coordinates, and constraints that reference back to the sampled primitives. As samples from the model match the constraint graph representation used in standard CAD software, they may be directly imported, solved, and edited according to downstream design tasks. In addition, we condition the model on various contexts, including partial sketches (primers) and images of hand-drawn sketches. Evaluation of the proposed approach demonstrates its ability to synthesize realistic CAD sketches and its potential to aid the mechanical design workflow.

Recently, much progress has been made in unsupervised denoising learning. However, existing methods more or less rely on some assumptions on the signal and/or degradation model, which limits their practical performance. How to construct an optimal criterion for unsupervised denoising learning without any prior knowledge on the degradation model is still an open question. Toward answering this question, this work proposes a criterion for unsupervised denoising learning based on the optimal transport theory. This criterion has favorable properties, e.g., approximately maximal preservation of the information of the signal, whilst achieving perceptual reconstruction. Furthermore, though a relaxed unconstrained formulation is used in practical implementation, we prove that the relaxed formulation in theory has the same solution as the original constrained formulation. Experiments on synthetic and real-world data, including realistic photographic, microscopy, depth, and raw depth images, demonstrate that the proposed method even compares favorably with supervised methods, e.g., approaching the PSNR of supervised methods while having better perceptual quality. Particularly, for spatially correlated noise and realistic microscopy images, the proposed method not only achieves better perceptual quality but also has higher PSNR than supervised methods. Besides, it shows remarkable superiority in harsh practical conditions with complex noise, e.g., raw depth images. Code is available at https://github.com/wangweiSJTU/OTUR.

With the progress in AI-based facial forgery (i.e., deepfake), people are increasingly concerned about its abuse. Albeit effort has been made for training classification (also known as deepfake detection) models to recognize such forgeries, existing models suffer from poor generalization to unseen forgery technologies and high sensitivity to changes in image/video quality. In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities. We believe training with samples that are adversarially crafted to attack the classification models improves the generalization ability considerably. Considering that AI-based face manipulation often leads to high-frequency artifacts that can be easily spotted by models yet difficult to generalize, we further propose a new adversarial training method that attempts to blur out these specific artifacts, by introducing pixel-wise Gaussian blurring models. With adversarial training, the classification models are forced to learn more discriminative and generalizable features, and the effectiveness of our method can be verified by plenty of empirical evidence. Our code will be made publicly available.

Mean field theory provides an effective way of scaling multiagent reinforcement learning algorithms to environments with many agents that can be abstracted by a virtual mean agent. In this paper, we extend mean field multiagent algorithms to multiple types. The types enable the relaxation of a core assumption in mean field reinforcement learning, which is that all agents in the environment are playing almost similar strategies and have the same goal. We conduct experiments on three different testbeds for the field of many agent reinforcement learning, based on the standard MAgents framework. We consider two different kinds of mean field environments: a) Games where agents belong to predefined types that are known a priori and b) Games where the type of each agent is unknown and therefore must be learned based on observations. We introduce new algorithms for each type of game and demonstrate their superior performance over state of the art algorithms that assume that all agents belong to the same type and other baseline algorithms in the MAgent framework.

Simulators offer the possibility of safe, low-cost development of self-driving systems. However, current driving simulators exhibit na\"ive behavior models for background traffic. Hand-tuned scenarios are typically added during simulation to induce safety-critical situations. An alternative approach is to adversarially perturb the background traffic trajectories. In this paper, we study this approach to safety-critical driving scenario generation using the CARLA simulator. We use a kinematic bicycle model as a proxy to the simulator's true dynamics and observe that gradients through this proxy model are sufficient for optimizing the background traffic trajectories. Based on this finding, we propose KING, which generates safety-critical driving scenarios with a 20% higher success rate than black-box optimization. By solving the scenarios generated by KING using a privileged rule-based expert algorithm, we obtain training data for an imitation learning policy. After fine-tuning on this new data, we show that the policy becomes better at avoiding collisions. Importantly, our generated data leads to reduced collisions on both held-out scenarios generated via KING as well as traditional hand-crafted scenarios, demonstrating improved robustness.

Time series classification is one of the very popular machine learning tasks. In this paper, we explore the application of Hidden Markov Model (HMM) for time series classification. We distinguish between two modes of HMM application. The first, in which a single model is built for each class. The second, in which one HMM is built for each time series. We then transfer both approaches for classifier construction to the domain of Fuzzy Cognitive Maps. The identified four models, HMM NN (HMM, one per series), HMM 1C (HMM, one per class), FCM NN, and FCM 1C are then studied in a series of experiments. We compare the performance of different models and investigate the impact of their hyperparameters on the time series classification accuracy. The empirical evaluation shows a clear advantage of the one-model-per-series approach. The results show that the choice between HMM and FCM should be dataset-dependent.

Stock prices are highly volatile and sudden changes in trends are often very problematic for traditional forecasting models to handle. The standard Long Short Term Memory (LSTM) networks are regarded as the state-of-the-art models for such predictions. But, these models fail to handle sudden and drastic changes in the price trend. Moreover, there are some inherent constraints with the open, high, low and close (OHLC) prices of the stocks. Literature lacks the study on the inherent property of OHLC prices. We argue that predicting the OHLC prices for the next day is much more informative than predicting the trends of the stocks as the trend is mostly calculated using these OHLC prices only. The problem mainly is focused on Buy-Today Sell-Tomorrow (BTST) trading. In this regard, AEs when pre-trained with the stock prices, may be beneficial. A novel framework is proposed where a pre-trained encoder is cascaded in front of the multi-task predictor network. This hybrid network can leverage the power of a combination of networks and can both handle the OHLC constraints as well as capture any sudden drastic changes in the prices. It is seen that such a network is much more efficient at predicting stock prices. The experiments have been extended to recommend the most profitable and most overbought stocks on the next day. The model has been tested for multiple Indian companies and it is found that the recommendations from the proposed model have not resulted in a single loss for a test period of 300 days.

We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past. The common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information. We propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions. We derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR. Numerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them.

Conventional reinforcement learning (RL) typically determines an appropriate primitive action at each timestep. However, by using a proper macro action, defined as a sequence of primitive actions, an agent is able to bypass intermediate states to a farther state and facilitate its learning procedure. The problem we would like to investigate is what associated beneficial properties that macro actions may possess. In this paper, we unveil the properties of reusability and transferability of macro actions. The first property, reusability, means that a macro action generated along with one RL method can be reused by another RL method for training, while the second one, transferability, means that a macro action can be utilized for training agents in similar environments with different reward settings. In our experiments, we first generate macro actions along with RL methods. We then provide a set of analyses to reveal the properties of reusability and transferability of the generated macro actions.

Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of systems. While information transfer analysis has booming applications in both science and engineering fields, critical mysteries about its foundations remain unsolved. Fundamental yet difficult questions concern how information transfer and causal relation originate, what they depend on, how they differ from each other, and if they are created by a unified and general quantity. These questions essentially determine the validity of causal relation measurement via information transfer. Here we pursue to lay a complete theoretical basis of information transfer and causal relation. Beyond the well-known relations between these concepts that conditionally hold, we demonstrate that information transfer and causal relation universally originate from specific information synergy and redundancy phenomena characterized by high-order mutual information. More importantly, our theory analytically explains the mechanisms for information transfer and causal relation to originate, vanish, and differ from each other. Moreover, our theory naturally defines the effect sizes of information transfer and causal relation based on high-dimensional coupling events. These results may provide a unified view of information, synergy, and causal relation to bridge Pearl's causal inference theory in computer science and information transfer analysis in physics.

Multi-instrument Automatic Music Transcription (AMT), or the decoding of a musical recording into semantic musical content, is one of the holy grails of Music Information Retrieval. Current AMT approaches are restricted to piano and (some) guitar recordings, due to difficult data collection. In order to overcome data collection barriers, previous AMT approaches attempt to employ musical scores in the form of a digitized version of the same song or piece. The scores are typically aligned using audio features and strenuous human intervention to generate training labels. We introduce NoteEM, a method for simultaneously training a transcriber and aligning the scores to their corresponding performances, in a fully-automated process. Using this unaligned supervision scheme, complemented by pseudo-labels and pitch-shift augmentation, our method enables training on in-the-wild recordings with unprecedented accuracy and instrumental variety. Using only synthetic data and unaligned supervision, we report SOTA note-level accuracy of the MAPS dataset, and large favorable margins on cross-dataset evaluations. We also demonstrate robustness and ease of use; we report comparable results when training on a small, easily obtainable, self-collected dataset, and we propose alternative labeling to the MusicNet dataset, which we show to be more accurate. Our project page is available at https://benadar293.github.io

Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn. One can use process data for many downstream tasks such as learning outcome prediction and automatically delivering personalized intervention. However, analyzing process data is challenging since the specific format of process data varies a lot depending on different learning/testing scenarios. In this paper, we propose a framework for learning representations of educational process data that is applicable across many different learning scenarios. Our framework consists of a pre-training step that uses BERT-type objectives to learn representations from sequential process data and a fine-tuning step that further adjusts these representations on downstream prediction tasks. We apply our framework to the 2019 nation's report card data mining competition dataset that consists of student problem-solving process data and detail the specific models we use in this scenario. We conduct both quantitative and qualitative experiments to show that our framework results in process data representations that are both predictive and informative.

Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points. Differentially Private Stochastic Gradient Descent (DP-SGD), the most popular DP training method, realizes this protection by injecting noise during training. However previous works have found that DP-SGD often leads to a significant degradation in performance on standard image classification benchmarks. Furthermore, some authors have postulated that DP-SGD inherently performs poorly on large models, since the norm of the noise required to preserve privacy is proportional to the model dimension. In contrast, we demonstrate that DP-SGD on over-parameterized models can perform significantly better than previously thought. Combining careful hyper-parameter tuning with simple techniques to ensure signal propagation and improve the convergence rate, we obtain a new SOTA on CIFAR-10 of 81.4% under (8, 10^{-5})-DP using a 40-layer Wide-ResNet, improving over the previous SOTA of 71.7%. When fine-tuning a pre-trained 200-layer Normalizer-Free ResNet, we achieve a remarkable 77.1% top-1 accuracy on ImageNet under (1, 8*10^{-7})-DP, and achieve 81.1% under (8, 8*10^{-7})-DP. This markedly exceeds the previous SOTA of 47.9% under a larger privacy budget of (10, 10^{-6})-DP. We believe our results are a significant step towards closing the accuracy gap between private and non-private image classification.

The projection of sample measurements onto a reconstruction space represented by a basis on a regular grid is a powerful and simple approach to estimate a probability density function. In this paper, we focus on Riesz bases and propose a projection operator that, in contrast to previous works, guarantees the bona fide properties for the estimate, namely, non-negativity and total probability mass $1$. Our bona fide projection is defined as a convex problem. We propose solution techniques and evaluate them. Results suggest an improved performance, specifically in circumstances prone to rippling effects.

Understanding what can be learned from experiments is central to scientific progress. In this work, we use a learning-theoretic perspective to study the task of learning physical operations in a quantum machine when all operations (state preparation, dynamics, and measurement) are a priori unknown. We prove that, without any prior knowledge, if one can explore the full quantum state space by composing the operations, then every operation can be learned. When one cannot explore the full state space but all operations are approximately known and noise in Clifford gates is gate-independent, we find an efficient algorithm for learning all operations up to a single unlearnable parameter characterizing the fidelity of the initial state. For learning a noise channel on Clifford gates to a fixed accuracy, our algorithm uses quadratically fewer experiments than previously known protocols. Under more general conditions, the true description of the noise can be unlearnable; for example, we prove that no benchmarking protocol can learn gate-dependent Pauli noise on Clifford+T gates even under perfect state preparation and measurement. Despite not being able to learn the noise, we show that a noisy quantum computer that performs entangled measurements on multiple copies of an unknown state can yield a large advantage in learning properties of the state compared to a noiseless device that measures individual copies and then processes the measurement data using a classical computer. Concretely, we prove that noisy quantum computers with two-qubit gate error rate $\epsilon$ can achieve a learning task using $N$ copies of the state, while $N^{\Omega(1/\epsilon)}$ copies are required classically.

Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources, such as beamformers, transmit power, and computation time. However, due to the unique constraints on the phase shift, and massive numbers of reflecting units and users in large-scale networks, the resulting optimization problems are challenging to solve. This paper provides a review of current optimization methods and artificial intelligence-based methods for handling the constraints imposed by RIS and compares them in terms of solution quality and computational complexity. Future challenges in phase shift optimization involving RISs are also described and potential solutions are discussed.

Learning a new concept from one example is a superior function of human brain and it is drawing attention in the field of machine learning as one-shot learning task. In this paper, we propose the simplest method for this task, named Direct ONE-shot learning (DONE). DONE adds a new class to a pretrained deep neural network (DNN) classifier with neither training optimization nor other-classes modification. DONE is inspired by Hebbian theory and directly uses the neural activity input of the final dense layer obtained from a data that belongs to the new additional class as the connectivity weight (synaptic strength) with a newly-provided-output neuron for the new class. DONE requires just one inference for obtaining the output of the final dense layer and its procedure is simple, deterministic, not requiring parameter tuning and hyperparameters. The performance of DONE depends entirely on the pretrained DNN model used as a backbone model, and we confirmed that DONE with a well-trained backbone model performs a practical-level accuracy. DONE has some advantages including a DNN's practical use that is difficult to spend high cost for a training, an evaluation of existing DNN models, and the understanding of the brain. DONE might be telling us one-shot learning is an easy task that can be achieved by a simple principle not only for humans but also for current well-trained DNN models.

List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners with many lines-of-response (LORs) and additional information such as time-of-flight and depth-of-interaction. Deep learning is one possible solution to enhance the quality of PET image reconstruction. However, the application of deep learning techniques to list-mode PET image reconstruction have not been progressed because list data is a sequence of bit codes and unsuitable for processing by convolutional neural networks (CNN). In this study, we propose a novel list-mode PET image reconstruction method using an unsupervised CNN called deep image prior (DIP) and a framework of alternating direction method of multipliers. The proposed list-mode DIP reconstruction (LM-DIPRecon) method alternatively iterates regularized list-mode dynamic row action maximum likelihood algorithm (LM-DRAMA) and magnetic resonance imaging conditioned DIP (MR-DIP). We evaluated LM-DIPRecon using both simulation and clinical data, and it achieved sharper images and better tradeoff curves between contrast and noise than the LM-DRAMA and MR-DIP. These results indicated that the LM-DIPRecon is useful for quantitative PET imaging with limited events. In addition, as list data has finer temporal information than dynamic sinograms, list-mode deep image prior reconstruction is expected to be useful for 4D PET imaging and motion correction.

Various attack methods against recommender systems have been proposed in the past years, and the security issues of recommender systems have drawn considerable attention. Traditional attacks attempt to make target items recommended to as many users as possible by poisoning the training data. Benifiting from the feature of protecting users' private data, federated recommendation can effectively defend such attacks. Therefore, quite a few works have devoted themselves to developing federated recommender systems. For proving current federated recommendation is still vulnerable, in this work we probe to design attack approaches targeting deep learning based recommender models in federated learning scenarios. Specifically, our attacks generate poisoned gradients for manipulated malicious users to upload based on two strategies (i.e., random approximation and hard user mining). Extensive experiments show that our well-designed attacks can effectively poison the target models, and the attack effectiveness sets the state-of-the-art.

There is recent interest in using model hubs, a collection of pre-trained models, in computer vision tasks. To utilize the model hub, we first select a source model and then adapt the model for the target to compensate for differences. While there is yet limited research on a model selection and adaption for computer vision tasks, this holds even more for the field of renewable power. At the same time, it is a crucial challenge to provide forecasts for the increasing demand for power forecasts based on weather features from a numerical weather prediction. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent results from the field of computer vision on six datasets. We adopt models based on data from different seasons and limit the amount of training data. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach. In fact, with more than 30 days of training data, both proposed model combination techniques achieve similar results to those models trained with a full year of training data.

We investigate optimal order execution problems in discrete time with instantaneous price impact and stochastic resilience. First, in the setting of linear transient price impact we derive a closed-form recursion for the optimal strategy, extending the deterministic results from Obizhaeva and Wang (J Financial Markets, 2013). Second, we develop a numerical algorithm based on dynamic programming and deep learning for the case of nonlinear transient price impact as proposed by Bouchaud et al. (Quant. Finance, 2004). Specifically, we utilize an actor-critic framework that constructs two neural-network (NN) surrogates for the value function and the feedback control. The flexible scalability of NN functional approximators enables parametric learning, i.e., incorporating several model or market parameters as part of the input space. Precise calibration of price impact, resilience, etc., is known to be extremely challenging and hence it is critical to understand sensitivity of the execution policy to these parameters. Our NN learner organically scales across multiple input dimensions and is shown to accurately approximate optimal strategies across a wide range of parameter configurations. We provide a fully reproducible Jupyter Notebook with our NN implementation, which is of independent pedagogical interest, demonstrating the ease of use of NN surrogates in (parametric) stochastic control problems.

Identifying sleep stages and patterns is an essential part of diagnosing and treating sleep disorders. With the advancement of smart technologies, sensor data related to sleeping patterns can be captured easily. In this paper, we propose a Convolution Neural Network (CNN) architecture that improves the classification performance. In particular, we benchmark the classification performance from different methods, including traditional machine learning methods such as Logistic Regression (LR), Decision Trees (DT), k-Nearest Neighbour (k-NN), Naive Bayes (NB) and Support Vector Machine (SVM), on 3 publicly available sleep datasets. The accuracy, sensitivity, specificity, precision, recall, and F-score are reported and will serve as a baseline to simulate the research in this direction in the future.

Recent deep learning Text-to-Speech (TTS) systems have achieved impressive performance by generating speech close to human parity. However, they suffer from training stability issues as well as incorrect alignment of the intermediate acoustic representation with the input text sequence. In this work, we introduce Regotron, a regularized version of Tacotron2 which aims to alleviate the training issues and at the same time produce monotonic alignments. Our method augments the vanilla Tacotron2 objective function with an additional term, which penalizes non-monotonic alignments in the location-sensitive attention mechanism. By properly adjusting this regularization term we show that the loss curves become smoother, and at the same time Regotron consistently produces monotonic alignments in unseen examples even at an early stage (13\% of the total number of epochs) of its training process, whereas the fully converged Tacotron2 fails to do so. Moreover, our proposed regularization method has no additional computational overhead, while reducing common TTS mistakes and achieving slighlty improved speech naturalness according to subjective mean opinion scores (MOS) collected from 50 evaluators.

Multi-player multi-armed bandits (MMAB) study how decentralized players cooperatively play the same multi-armed bandit so as to maximize their total cumulative rewards. Existing MMAB models mostly assume when more than one player pulls the same arm, they either have a collision and obtain zero rewards, or have no collision and gain independent rewards, both of which are usually too restrictive in practical scenarios. In this paper, we propose an MMAB with shareable resources as an extension to the collision and non-collision settings. Each shareable arm has finite shareable resources and a "per-load" reward random variable, both of which are unknown to players. The reward from a shareable arm is equal to the "per-load" reward multiplied by the minimum between the number of players pulling the arm and the arm's maximal shareable resources. We consider two types of feedback: sharing demand information (SDI) and sharing demand awareness (SDA), each of which provides different signals of resource sharing. We design the DPE-SDI and SIC-SDA algorithms to address the shareable arm problem under these two cases of feedback respectively and prove that both algorithms have logarithmic regrets that are tight in the number of rounds. We conduct simulations to validate both algorithms' performance and show their utilities in wireless networking and edge computing.

There is increasing interest in the use of HPC machines for urgent workloads to help tackle disasters as they unfold. Whilst batch queue systems are not ideal in supporting such workloads, many disadvantages can be worked around by accurately predicting when a waiting job will start to run. However there are numerous challenges in achieving such a prediction with high accuracy, not least because the queue's state can change rapidly and depend upon many factors. In this work we explore a novel machine learning approach for predicting queue wait times, hypothesising that such a model can capture the complex behaviour resulting from the queue policy and other interactions to generate accurate job start times. For ARCHER2 (HPE Cray EX), Cirrus (HPE 8600) and 4-cabinet (HPE Cray EX) we explore how different machine learning approaches and techniques improve the accuracy of our predictions, comparing against the estimation generated by Slurm. We demonstrate that our techniques deliver the most accurate predictions across our machines of interest, with the result of this work being the ability to predict job start times within one minute of the actual start time for around 65\% of jobs on ARCHER2 and 4-cabinet, and 76\% of jobs on Cirrus. When compared against what Slurm can deliver, this represents around 3.8 times better accuracy on ARCHER2 and 18 times better for Cirrus. Furthermore our approach can accurately predicting the start time for three quarters of all job within ten minutes of the actual start time on ARCHER2 and 4-cabinet, and for 90\% of jobs on Cirrus. Whilst the driver of this work has been to better facilitate placement of urgent workloads across HPC machines, the insights gained can be used to provide wider benefits to users and also enrich existing batch queue systems and inform policy too.

Green's function plays a significant role in both theoretical analysis and numerical computing of partial differential equations (PDEs). However, in most cases, Green's function is difficult to compute. The troubles arise in the following three folds. Firstly, compared with the original PDE, the dimension of Green's function is doubled, making it impossible to be handled by traditional mesh-based methods. Secondly, Green's function usually contains singularities which increase the difficulty to get a good approximation. Lastly, the computational domain may be very complex or even unbounded. To override these problems, we leverage the fundamental solution, boundary integral method and neural networks to develop a new method for computing Green's function with high accuracy in this paper. We focus on Green's function of Poisson and Helmholtz equations in bounded domains, unbounded domains. We also consider Poisson equation and Helmholtz domains with interfaces. Extensive numerical experiments illustrate the efficiency and the accuracy of our method for solving Green's function. In addition, we also use the Green's function calculated by our method to solve a class of PDE, and also obtain high-precision solutions, which shows the good generalization ability of our method on solving PDEs.

Graph matching is an important problem that has received widespread attention, especially in the field of computer vision. Recently, state-of-the-art methods seek to incorporate graph matching with deep learning. However, there is no research to explain what role the graph matching algorithm plays in the model. Therefore, we propose an approach integrating a MILP formulation of the graph matching problem. This formulation is solved to optimal and it provides inherent baseline. Meanwhile, similar approaches are derived by releasing the optimal guarantee of the graph matching solver and by introducing a quality level. This quality level controls the quality of the solutions provided by the graph matching solver. In addition, several relaxations of the graph matching problem are put to the test. Our experimental evaluation gives several theoretical insights and guides the direction of deep graph matching methods.

Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA-seq HTS is required to enrich single-cell data meaningfully. We introduce a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with a transfer learning scheme and demonstrate how training on existing bulk RNA-seq HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating targeted drug discovery.

An important goal in the field of human-AI interaction is to help users more appropriately trust AI systems' decisions. A situation in which the user may particularly benefit from more appropriate trust is when the AI receives anomalous input or provides anomalous output. To the best of our knowledge, this is the first work towards understanding how anomaly alerts may contribute to appropriate trust of AI. In a formative mixed-methods study with 4 radiologists and 4 other physicians, we explore how AI alerts for anomalous input, very high and low confidence, and anomalous saliency-map explanations affect users' experience with mockups of an AI clinical decision support system (CDSS) for evaluating chest x-rays for pneumonia. We find evidence suggesting that the four anomaly alerts are desired by non-radiologists, and the high-confidence alerts are desired by both radiologists and non-radiologists. In a follow-up user study, we investigate how high- and low-confidence alerts affect the accuracy and thus appropriate trust of 33 radiologists working with AI CDSS mockups. We observe that these alerts do not improve users' accuracy or experience and discuss potential reasons why.

Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records are valuable resources capturing unique information, but are seldom used to their full potential. We explore conventional and deep machine learning methods to assess violence risk in psychiatric patients using practitioner notes. The performance of our best models is comparable to the currently used questionnaire-based method, with an area under the Receiver Operating Characteristic curve of approximately 0.8. We find that the deep-learning model BERTje performs worse than conventional machine learning methods. We also evaluate our data and our classifiers to understand the performance of our models better. This is particularly important for the applicability of evaluated classifiers to new data, and is also of great interest to practitioners, due to the increased availability of new data in electronic format.

Storm surge is a major natural hazard for coastal regions, responsible both for significant property damage and loss of life. Accurate, efficient models of storm surge are needed both to assess long-term risk and to guide emergency management decisions. While high-fidelity ocean circulation models such as the ADvanced CIRCulation (ADCIRC) model can accurately predict storm surge, they are very computationally expensive. Consequently, there have been a number of efforts in recent years to develop data-driven surrogate models for storm surge. While these models can attain good accuracy and are highly efficient, they are often limited to a small geographical region and a fixed set of output locations. We develop a novel surrogate model for peak storm surge prediction based on gradient boosting. Unlike most surrogate approaches, our model is not explicitly constrained to a fixed set of output locations or specific geographical region. The model is trained with a database of 446 synthetic storms that make landfall on the Texas coast and obtains a mean absolute error of 0.25 meters. We additionally present a test of the model on Hurricanes Ike (2008) and Harvey (2017).

The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck in Federated Learning (FL). In this work, we propose a general recipe, FedShuffle, that better utilizes the local updates in FL, especially in the heterogeneous regime. Unlike many prior works, FedShuffle does not assume any uniformity in the number of updates per device. Our FedShuffle recipe comprises four simple-yet-powerful ingredients: 1) local shuffling of the data, 2) adjustment of the local learning rates, 3) update weighting, and 4) momentum variance reduction (Cutkosky and Orabona, 2019). We present a comprehensive theoretical analysis of FedShuffle and show that both theoretically and empirically, our approach does not suffer from the objective function mismatch that is present in FL methods which assume homogeneous updates in heterogeneous FL setups, e.g., FedAvg (McMahan et al., 2017). In addition, by combining the ingredients above, FedShuffle improves upon FedNova (Wang et al., 2020), which was previously proposed to solve this mismatch. We also show that FedShuffle with momentum variance reduction can improve upon non-local methods under a Hessian similarity assumption. Finally, through experiments on synthetic and real-world datasets, we illustrate how each of the four ingredients used in FedShuffle helps improve the use of local updates in FL.

Plackett-Luce gradient estimation enables the optimization of stochastic ranking models within feasible time constraints through sampling techniques. Unfortunately, the computational complexity of existing methods does not scale well with the length of the rankings, i.e. the ranking cutoff, nor with the item collection size. In this paper, we introduce the novel PL-Rank-3 algorithm that performs unbiased gradient estimation with a computational complexity comparable to the best sorting algorithms. As a result, our novel learning-to-rank method is applicable in any scenario where standard sorting is feasible in reasonable time. Our experimental results indicate large gains in the time required for optimization, without any loss in performance. For the field, our contribution could potentially allow state-of-the-art learning-to-rank methods to be applied to much larger scales than previously feasible.

Hyperspectral images, which store a hundred or more spectral bands of reflectance, have become an important data source in natural and social sciences. Hyperspectral images are often generated in large quantities at a relatively coarse spatial resolution. As such, unsupervised machine learning algorithms incorporating known structure in hyperspectral imagery are needed to analyze these images automatically. This work introduces the Spatial-Spectral Image Reconstruction and Clustering with Diffusion Geometry (DSIRC) algorithm for partitioning highly mixed hyperspectral images. DSIRC reduces measurement noise through a shape-adaptive reconstruction procedure. In particular, for each pixel, DSIRC locates spectrally correlated pixels within a data-adaptive spatial neighborhood and reconstructs that pixel's spectral signature using those of its neighbors. DSIRC then locates high-density, high-purity pixels far in diffusion distance (a data-dependent distance metric) from other high-density, high-purity pixels and treats these as cluster exemplars, giving each a unique label. Non-modal pixels are assigned the label of their diffusion distance-nearest neighbor of higher density and purity that is already labeled. Strong numerical results indicate that incorporating spatial information through image reconstruction substantially improves the performance of pixel-wise clustering.

Probability distributions supported on the simplex enjoy a wide range of applications across statistics and machine learning. Recently, a novel family of such distributions has been discovered: the continuous categorical. This family enjoys remarkable mathematical simplicity; its density function resembles that of the Dirichlet distribution, but with a normalizing constant that can be written in closed form using elementary functions only. In spite of this mathematical simplicity, our understanding of the normalizing constant remains far from complete. In this work, we characterize the numerical behavior of the normalizing constant and we present theoretical and methodological advances that can, in turn, help to enable broader applications of the continuous categorical distribution. Our code is available at https://github.com/cunningham-lab/cb_and_cc/.

Non-Hermitian systems offer new platforms for unusual physical properties that can be flexibly manipulated by redistribution of the real and imaginary parts of refractive indices, whose presence breaks conventional wave propagation symmetries, leading to asymmetric reflection and symmetric transmission with respect to the wave propagation direction. Here, we use supervised and unsupervised learning techniques for knowledge acquisition in non-Hermitian systems which accelerate the inverse design process. In particular, we construct a deep learning model that relates the transmission and asymmetric reflection in non-conservative settings and proposes sub-manifold learning to recognize non-Hermitian features from transmission spectra. The developed deep learning framework determines the feasibility of a desired spectral response for a given structure and uncovers the role of effective gain-loss parameters to tailor the spectral response. These findings pave the way for intelligent inverse design and shape our understanding of the physical mechanism in general non-Hermitian systems.

Different from large-scale platforms such as Taobao and Amazon, CVR modeling in small-scale recommendation scenarios is more challenging due to the severe Data Distribution Fluctuation (DDF) issue. DDF prevents existing CVR models from being effective since 1) several months of data are needed to train CVR models sufficiently in small scenarios, leading to considerable distribution discrepancy between training and online serving; and 2) e-commerce promotions have significant impacts on small scenarios, leading to distribution uncertainty of the upcoming time period. In this work, we propose a novel CVR method named MetaCVR from a perspective of meta learning to address the DDF issue. Firstly, a base CVR model which consists of a Feature Representation Network (FRN) and output layers is designed and trained sufficiently with samples across months. Then we treat time periods with different data distributions as different occasions and obtain positive and negative prototypes for each occasion using the corresponding samples and the pre-trained FRN. Subsequently, a Distance Metric Network (DMN) is devised to calculate the distance metrics between each sample and all prototypes to facilitate mitigating the distribution uncertainty. At last, we develop an Ensemble Prediction Network (EPN) which incorporates the output of FRN and DMN to make the final CVR prediction. In this stage, we freeze the FRN and train the DMN and EPN with samples from recent time period, therefore effectively easing the distribution discrepancy. To the best of our knowledge, this is the first study of CVR prediction targeting the DDF issue in small-scale recommendation scenarios. Experimental results on real-world datasets validate the superiority of our MetaCVR and online A/B test also shows our model achieves impressive gains of 11.92% on PCVR and 8.64% on GMV.

Consumer-grade music recordings such as those captured by mobile devices typically contain distortions in the form of background noise, reverb, and microphone-induced EQ. This paper presents a deep learning approach to enhance low-quality music recordings by combining (i) an image-to-image translation model for manipulating audio in its mel-spectrogram representation and (ii) a music vocoding model for mapping synthetically generated mel-spectrograms to perceptually realistic waveforms. We find that this approach to music enhancement outperforms baselines which use classical methods for mel-spectrogram inversion and an end-to-end approach directly mapping noisy waveforms to clean waveforms. Additionally, in evaluating the proposed method with a listening test, we analyze the reliability of common audio enhancement evaluation metrics when used in the music domain.

Autonomous vehicle software is typically structured as a modular pipeline of individual components (e.g., perception, prediction, and planning) to help separate concerns into interpretable sub-tasks. Even when end-to-end training is possible, each module has its own set of objectives used for safety assurance, sample efficiency, regularization, or interpretability. However, intermediate objectives do not always align with overall system performance. For example, optimizing the likelihood of a trajectory prediction module might focus more on easy-to-predict agents than safety-critical or rare behaviors (e.g., jaywalking). In this paper, we present control-aware prediction objectives (CAPOs), to evaluate the downstream effect of predictions on control without requiring the planner be differentiable. We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories. Experimentally, we show our objectives improve overall system performance in suburban driving scenarios using the CARLA simulator.

The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional algorithms for training such models often require data to be stored on a single machine with compute performed by a single node, making them unsuitable for decentralised training on multiple devices. This deficiency has motivated the development of federated learning algorithms, which allow multiple data owners to train collaboratively and use a shared model whilst keeping local data private. However, many of these algorithms focus on obtaining point estimates of model parameters, rather than probabilistic estimates capable of capturing model uncertainty, which is essential in many applications. Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. In this paper we introduce partitioned variational inference (PVI), a general framework for performing VI in the federated setting. We develop new supporting theory for PVI, demonstrating a number of properties that make it an attractive choice for practitioners; use PVI to unify a wealth of fragmented, yet related literature; and provide empirical results that showcase the effectiveness of PVI in a variety of federated settings.

A rising number of botnet families have been successfully detected using deep learning architectures. While the variety of attacks increases, these architectures should become more robust against attacks. They have been proven to be very sensitive to small but well constructed perturbations in the input. Botnet detection requires extremely low false-positive rates (FPR), which are not commonly attainable in contemporary deep learning. Attackers try to increase the FPRs by making poisoned samples. The majority of recent research has focused on the use of model loss functions to build adversarial examples and robust models. In this paper, two LSTM-based classification algorithms for botnet classification with an accuracy higher than 98% are presented. Then, the adversarial attack is proposed, which reduces the accuracy to about 30%. Then, by examining the methods for computing the uncertainty, the defense method is proposed to increase the accuracy to about 70%. By using the deep ensemble and stochastic weight averaging quantification methods it has been investigated the uncertainty of the accuracy in the proposed methods.

Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel scheduler for real-time multimedia delivery in multipath systems based on an Actor-Critic (AC) RL algorithm. We focus on a challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) using multiple wireless paths. The scheduler acting as an RL agent learns in real-time the optimal policy for path selection, path rate allocation and redundancy estimation for flow protection. The scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The simulation results show that our scheduler can target a very low loss rate at the receiver by dynamically adapting in real-time the scheduling policy to the path conditions without performing training or relying on prior knowledge of network channel models.

Long-tail learning is the problem of learning under skewed label distributions, which pose a challenge for standard learners. Several recent approaches for the problem have proposed enforcing a suitable margin in logit space. Such techniques are intuitive analogues of the guiding principle behind SVMs, and are equally applicable to linear models and neural models. However, when applied to neural models, such techniques do not explicitly control the geometry of the learned embeddings. This can be potentially sub-optimal, since embeddings for tail classes may be diffuse, resulting in poor generalization for these classes. We present Embedding and Logit Margins (ELM), a unified approach to enforce margins in logit space, and regularize the distribution of embeddings. This connects losses for long-tail learning to proposals in the literature on metric embedding, and contrastive learning. We theoretically show that minimising the proposed ELM objective helps reduce the generalisation gap. The ELM method is shown to perform well empirically, and results in tighter tail class embeddings.

In Federated Learning a number of clients collaborate to train a model without sharing their data. Client models are optimized locally and are communicated through a central hub called server. A major challenge is to deal with heterogeneity among clients' data which causes the local optimization to drift away with respect to the global objective. In order to estimate and therefore remove this drift, variance reduction techniques have been incorporated into Federated Learning optimization recently. However, the existing solutions propagate the error of their estimations, throughout the optimization trajectory which leads to inaccurate approximations of the clients' drift and ultimately failure to remove them properly. In this paper, we address this issue by introducing an adaptive algorithm that efficiently reduces clients' drift. Compared to the previous works on adapting variance reduction to Federated Learning, our approach uses less or the same level of communication bandwidth, computation or memory. Additionally, it addresses the instability problem--prevalent in prior work, caused by increasing norm of the estimates which makes our approach a much more practical solution for large scale Federated Learning settings. Our experimental results demonstrate that the proposed algorithm converges significantly faster and achieves higher accuracy compared to the baselines in an extensive set of Federated Learning benchmarks.

Deep learning has shown its human-level performance in various applications. However, current deep learning models are characterised by catastrophic forgetting of old knowledge when learning new classes. This poses a challenge particularly in intelligent diagnosis systems where initially only training data of a limited number of diseases are available. In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning built on a fixed pre-trained feature extractor. In this model, knowledge of each old class can be compactly represented by a collection of statistical distributions, e.g. with Gaussian mixture models, and naturally kept from forgetting in continual learning over time. Unlike existing class-incremental learning methods, the proposed approach is not sensitive to the continual learning process and can be additionally well applied to the data-incremental learning scenario. Experiments on multiple medical and natural image classification tasks showed that the proposed approach outperforms state-of-the-art approaches which even keep some images of old classes during continual learning of new classes.

In recommendation systems, the choice of loss function is critical since a good loss may significantly improve the model performance. However, manually designing a good loss is a big challenge due to the complexity of the problem. A large fraction of previous work focuses on handcrafted loss functions, which needs significant expertise and human effort. In this paper, inspired by the recent development of automated machine learning, we propose an automatic loss function generation framework, AutoLossGen, which is able to generate loss functions directly constructed from basic mathematical operators without prior knowledge on loss structure. More specifically, we develop a controller model driven by reinforcement learning to generate loss functions, and develop iterative and alternating optimization schedule to update the parameters of both the controller model and the recommender model. One challenge for automatic loss generation in recommender systems is the extreme sparsity of recommendation datasets, which leads to the sparse reward problem for loss generation and search. To solve the problem, we further develop a reward filtering mechanism for efficient and effective loss generation. Experimental results show that our framework manages to create tailored loss functions for different recommendation models and datasets, and the generated loss gives better recommendation performance than commonly used baseline losses. Besides, most of the generated losses are transferable, i.e., the loss generated based on one model and dataset also works well for another model or dataset. Source code of the work is available at https://github.com/rutgerswiselab/AutoLossGen.

We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems, where a central server aggregates local models of $N \in \mathbb{N}$ distributed users, each of size $L \in \mathbb{N}$, trained on their local data, in a privacy-preserving manner. SwiftAgg+ can significantly reduce the communication overheads without any compromise on security, and achieve optimal communication loads within diminishing gaps. Specifically, in presence of at most $D$ dropout users, SwiftAgg+ achieves a per-user communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ and a server communication load of $(1+\mathcal{O}(\frac{1}{N}))L$, with a worst-case information-theoretic security guarantee, against any subset of up to $T$ semi-honest users who may also collude with the curious server. Moreover, the proposed SwiftAgg+ allows for a flexible trade-off between communication loads and the number of active communication links. In particular, for any $K\in\mathbb{N}$, SwiftAgg+ can achieve the server communication load of $(1+\frac{T}{K})L$, and per-user communication load of up to $(1+\frac{T+D}{K})L$, where the number of pair-wise active connections in the network is $\frac{N}{2}(K+T+D+1)$.

Bayesian learning using Gaussian processes provides a foundational framework for making decisions in a manner that balances what is known with what could be learned by gathering data. In this dissertation, we develop techniques for broadening the applicability of Gaussian processes. This is done in two ways. Firstly, we develop pathwise conditioning techniques for Gaussian processes, which allow one to express posterior random functions as prior random functions plus a dependent update term. We introduce a wide class of efficient approximations built from this viewpoint, which can be randomly sampled once in advance, and evaluated at arbitrary locations without any subsequent stochasticity. This key property improves efficiency and makes it simpler to deploy Gaussian process models in decision-making settings. Secondly, we develop a collection of Gaussian process models over non-Euclidean spaces, including Riemannian manifolds and graphs. We derive fully constructive expressions for the covariance kernels of scalar-valued Gaussian processes on Riemannian manifolds and graphs. Building on these ideas, we describe a formalism for defining vector-valued Gaussian processes on Riemannian manifolds. The introduced techniques allow all of these models to be trained using standard computational methods. In total, these contributions make Gaussian processes easier to work with and allow them to be used within a wider class of domains in an effective and principled manner. This, in turn, makes it possible to potentially apply Gaussian processes to novel decision-making settings.

We propose a policy search approach to learn controllers from specifications given as Signal Temporal Logic (STL) formulae. The system model, which is unknown but assumed to be an affine control system, is learned together with the control policy. The model is implemented as two feedforward neural networks (FNNs) - one for the drift, and one for the control directions. To capture the history dependency of STL specifications, we use a recurrent neural network (RNN) to implement the control policy. In contrast to prevalent model-free methods, the learning approach proposed here takes advantage of the learned model and is more efficient. We use control barrier functions (CBFs) with the learned model to improve the safety of the system. We validate our algorithm via simulations and experiments. The results show that our approach can satisfy the given specification within very few system runs, and can be used for on-line control.

In the paper, we propose an effective and efficient Compositional Federated Learning (ComFedL) algorithm for solving a new compositional Federated Learning (FL) framework, which frequently appears in many data mining and machine learning problems with a hierarchical structure such as distributionally robust FL and model-agnostic meta learning (MAML). Moreover, we study the convergence analysis of our ComFedL algorithm under some mild conditions, and prove that it achieves a convergence rate of $O(\frac{1}{\sqrt{T}})$, where $T$ denotes the number of iteration. To the best of our knowledge, our new Compositional FL framework is the first work to bridge federated learning with composition stochastic optimization. In particular, we first transform the distributionally robust FL (i.e., a minimax optimization problem) into a simple composition optimization problem by using KL divergence regularization. At the same time, we also first transform the distribution-agnostic MAML problem (i.e., a minimax optimization problem) into a simple yet effective composition optimization problem. Finally, we apply two popular machine learning tasks, i.e., distributionally robust FL and MAML to demonstrate the effectiveness of our algorithm.

In this paper, we approach the problem of optimizing blackbox functions over large hybrid search spaces consisting of both combinatorial and continuous parameters. We demonstrate that previous evolutionary algorithms which rely on mutation-based approaches, while flexible over combinatorial spaces, suffer from a curse of dimensionality in high dimensional continuous spaces both theoretically and empirically, which thus limits their scope over hybrid search spaces as well. In order to combat this curse, we propose ES-ENAS, a simple and modular joint optimization procedure combining the class of sample-efficient smoothed gradient gradient techniques, commonly known as Evolutionary Strategies (ES), with combinatorial optimizers in a highly scalable and intuitive way, inspired by the one-shot or supernet paradigm introduced in Efficient Neural Architecture Search (ENAS). By doing so, we achieve significantly more sample efficiency, which we empirically demonstrate over synthetic benchmarks, and are further able to apply ES-ENAS for architecture search over popular RL benchmarks.

This paper explores the calibration of a classifier output score in binary classification problems. A calibrator is a function that maps the arbitrary classifier score, of a testing observation, onto $[0,1]$ to provide an estimate for the posterior probability of belonging to one of the two classes. Calibration is important for two reasons; first, it provides a meaningful score, that is the posterior probability; second, it puts the scores of different classifiers on the same scale for comparable interpretation. The paper presents three main contributions: (1) Introducing multi-score calibration, when more than one classifier provides a score for a single observation. (2) Introducing the idea that the classifier scores to a calibration process are nothing but features to a classifier, hence proposing expanding the classifier scores to higher dimensions to boost the calibrator's performance. (3) Conducting a massive simulation study, in the order of 24,000 experiments, that incorporates different configurations, in addition to experimenting on two real datasets from the cybersecurity domain. The results show that there is no overall winner among the different calibrators and different configurations. However, general advices for practitioners include the following: the Platt's calibrator~\citep{Platt1999ProbabilisticOutputsForSupport}, a version of the logistic regression that decreases bias for a small sample size, has a very stable and acceptable performance among all experiments; our suggested multi-score calibration provides better performance than single score calibration in the majority of experiments, including the two real datasets. In addition, expanding the scores can help in some experiments.

This letter is concerned with image classification with deep convolutional neural networks (CNNs). The focus is on the following question: given a set of candidate CNN models, how to select the right one with the best generalization property for the current task? Present model selection methods require access to a batch of labeled data for computing a pre-specified performance metric, such as the cross-entropy loss, the classification error rate, the negative log-likelihood. In many practical cases, labels are not available in time as labeling itself is a time-consuming and expensive task. To this end, this letter presents an approach to CNN model selection using only unlabeled data. This method is developed based on a principle termed consistent relative confidence. The effectiveness and efficiency of the proposed method are demonstrated by experiments using benchmark datasets.

Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that goes beyond existing mechanisms such as federated learning. Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems.

Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively. Despite no data being shared explicitly, recent studies showed that the mechanism could still leak sensitive information. Hence, secure aggregation is utilized in many real-world scenarios to prevent attribution to specific participants. In this paper, we focus on the quality of individual training datasets and show that such quality information could be inferred and attributed to specific participants even when secure aggregation is applied. Specifically, through a series of image recognition experiments, we infer the relative quality ordering of participants. Moreover, we apply the inferred quality information to detect misbehaviours, to stabilize training performance, and to measure the individual contributions of participants.

Computer vision algorithms have been prevalently utilized for 3-D road imaging and pothole detection for over two decades. Nonetheless, there is a lack of systematic survey articles on state-of-the-art (SoTA) computer vision techniques, especially deep learning models, developed to tackle these problems. This article first introduces the sensing systems employed for 2-D and 3-D road data acquisition, including camera(s), laser scanners, and Microsoft Kinect. Afterward, it thoroughly and comprehensively reviews the SoTA computer vision algorithms, including (1) classical 2-D image processing, (2) 3-D point cloud modeling and segmentation, and (3) machine/deep learning, developed for road pothole detection. This article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches: classical 2-D image processing-based and 3-D point cloud modeling and segmentation-based approaches have already become history; and Convolutional neural networks (CNNs) have demonstrated compelling road pothole detection results and are promising to break the bottleneck with the future advances in self/un-supervised learning for multi-modal semantic segmentation. We believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

Text detoxification is a style transfer task of creating neutral versions of toxic texts. In this paper, we use the concept of text editing to build a two-step tagging-based detoxification model using a parallel corpus of Russian texts. With this model, we achieved the best style transfer accuracy among all models in the RUSSE Detox shared task, surpassing larger sequence-to-sequence models.

Spectral methods offer a tractable, global framework for clustering in graphs via eigenvector computations on graph matrices. Hypergraph data, in which entities interact on edges of arbitrary size, poses challenges for matrix representations and therefore for spectral clustering. We study spectral clustering for nonuniform hypergraphs based on the hypergraph nonbacktracking operator. After reviewing the definition of this operator and its basic properties, we prove a theorem of Ihara-Bass type to enable faster computation of eigenpairs. We then propose an alternating algorithm for inference in a hypergraph stochastic blockmodel via linearized belief-propagation, offering proofs that both formalize and extend several previous results. We perform experiments in real and synthetic data that underscore the benefits of hypergraph methods over graph-based ones when interactions of different sizes carry different information about cluster structure. Through an analysis of our algorithm, we pose several conjectures about the limits of spectral methods and detectability in hypergraph stochastic blockmodels writ large.

Generative models such as the variational autoencoder (VAE) and the generative adversarial networks (GAN) have proven to be incredibly powerful for the generation of synthetic data that preserves statistical properties and utility of real-world datasets, especially in the context of image and natural language text. Nevertheless, until now, there has no successful demonstration of how to apply either method for generating useful physiological sensory data. The state-of-the-art techniques in this context have achieved only limited success. We present PHYSIOGAN, a generative model to produce high fidelity synthetic physiological sensor data readings. PHYSIOGAN consists of an encoder, decoder, and a discriminator. We evaluate PHYSIOGAN against the state-of-the-art techniques using two different real-world datasets: ECG classification and activity recognition from motion sensors datasets. We compare PHYSIOGAN to the baseline models not only the accuracy of class conditional generation but also the sample diversity and sample novelty of the synthetic datasets. We prove that PHYSIOGAN generates samples with higher utility than other generative models by showing that classification models trained on only synthetic data generated by PHYSIOGAN have only 10% and 20% decrease in their classification accuracy relative to classification models trained on the real data. Furthermore, we demonstrate the use of PHYSIOGAN for sensor data imputation in creating plausible results.

We consider the problem of personalized federated learning when there are known cluster structures within users. An intuitive approach would be to regularize the parameters so that users in the same cluster share similar model weights. The distances between the clusters can then be regularized to reflect the similarity between different clusters of users. We develop an algorithm that allows each cluster to communicate independently and derive the convergence results. We study a hierarchical linear model to theoretically demonstrate that our approach outperforms agents learning independently and agents learning a single shared weight. Finally, we demonstrate the advantages of our approach using both simulated and real-world data.

Semantic-descriptor-based Generalized Zero-Shot Learning (GZSL) poses challenges in recognizing novel classes in the test phase. The development of generative models enables current GZSL techniques to probe further into the semantic-visual link, culminating in a two-stage form that includes a generator and a classifier. However, existing generation-based methods focus on enhancing the generator's effect while neglecting the improvement of the classifier. In this paper, we first analyze of two properties of the generated pseudo unseen samples: bias and homogeneity. Then, we perform variational Bayesian inference to back-derive the evaluation metrics, which reflects the balance of the seen and unseen classes. As a consequence of our derivation, the aforementioned two properties are incorporated into the classifier training as seen-unseen priors via logit adjustment. The Zero-Shot Logit Adjustment further puts semantic-based classifiers into effect in generation-based GZSL. Our experiments demonstrate that the proposed technique achieves state-of-the-art when combined with the basic generator, and it can improve various generative zero-shot learning frameworks. Our codes are available on https://github.com/cdb342/IJCAI-2022-ZLA.

Due to data privacy constraints, data sharing among multiple centers is restricted. Continual learning, as one approach to peer-to-peer federated learning, can promote multicenter collaboration on deep learning algorithm development by sharing intermediate models instead of training data. This work aims to investigate the feasibility of continual learning for multicenter collaboration on an exemplary application of brain metastasis identification using DeepMedic. 920 T1 MRI contrast enhanced volumes are split to simulate multicenter collaboration scenarios. A continual learning algorithm, synaptic intelligence (SI), is applied to preserve important model weights for training one center after another. In a bilateral collaboration scenario, continual learning with SI achieves a sensitivity of 0.917, and naive continual learning without SI achieves a sensitivity of 0.906, while two models trained on internal data solely without continual learning achieve sensitivity of 0.853 and 0.831 only. In a seven-center multilateral collaboration scenario, the models trained on internal datasets (100 volumes each center) without continual learning obtain a mean sensitivity value of 0.725. With single-visit continual learning (i.e., the shared model visits each center only once during training), the sensitivity is improved to 0.788 and 0.849 without SI and with SI, respectively. With iterative continual learning (i.e., the shared model revisits each center multiple times during training), the sensitivity is further improved to 0.914, which is identical to the sensitivity using mixed data for training. Our experiments demonstrate that continual learning can improve brain metastasis identification performance for centers with limited data. This study demonstrates the feasibility of applying continual learning for peer-to-peer federated learning in multicenter collaboration.

Semi-supervised learning (SSL) has become an interesting research area due to its capacity for learning in scenarios where both labeled and unlabeled data are available. In this work, we focus on the task of transduction - when the objective is to label all data presented to the learner - with a mean-field approximation to the Potts model. Aiming at this particular task we study how classification results depend on $\beta$ and find that the optimal phase depends highly on the amount of labeled data available. In the same study, we also observe that more stable classifications regarding small fluctuations in $\beta$ are related to configurations of high probability and propose a tuning approach based on such observation. This method relies on a novel parameter $\gamma$ and we then evaluate two different values of the said quantity in comparison with classical methods in the field. This evaluation is conducted by changing the amount of labeled data available and the number of nearest neighbors in the similarity graph. Empirical results show that the tuning method is effective and allows NMF to outperform other approaches in datasets with fewer classes. In addition, one of the chosen values for $\gamma$ also leads to results that are more resilient to changes in the number of neighbors, which might be of interest to practitioners in the field of SSL.

Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services. Such systems should be able to enrol (E), verify (V), and identify (I) new and recurring users based on their personal information, e.g. postcode, name, and date of birth. In this work, we formalise the three authentication tasks and their evaluation protocols, and we present EVI, a challenging spoken multilingual dataset with 5,506 dialogues in English, Polish, and French. Our proposed models set the first competitive benchmarks, explore the challenges of multilingual natural language processing of spoken dialogue, and set directions for future research.

Inventory management in warehouses directly affects profits made by manufacturers. Particularly, large manufacturers produce a very large variety of products that are handled by a significantly large number of retailers. In such a case, the computational complexity of classical inventory management algorithms is inordinately large. In recent years, learning-based approaches have become popular for addressing such problems. However, previous studies have not been managed systems where both the number of products and retailers are large. This study proposes a reinforcement learning-based warehouse inventory management algorithm that can be used for supply chain systems where both the number of products and retailers are large. To solve the computational problem of handling large systems, we provide a means of approximate simulation of the system in the training phase. Our experiments on both real and artificial data demonstrate that our algorithm with approximated simulation can successfully handle large supply chain networks.

Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real applications becomes challenging due to the large labeled collections required during training. Several works have pursued solutions to overcome it by proposing strategies that can learn more for less, e.g., weakly and semi-supervised learning approaches. As these approaches do not usually address memorization and sensitivity to adversarial examples, this paper presents three deep metric learning approaches combined with Mixup for incomplete-supervision scenarios. We show that some state-of-the-art approaches in metric learning might not work well in such scenarios. Moreover, the proposed approaches outperform most of them in different datasets.

Adversarial training is an effective method to boost model robustness to malicious, adversarial attacks. However, such improvement in model robustness often leads to a significant sacrifice of standard performance on clean images. In many real-world applications such as health diagnosis and autonomous surgical robotics, the standard performance is more valued over model robustness against such extremely malicious attacks. This leads to the question: To what extent we can boost model robustness without sacrificing standard performance? This work tackles this problem and proposes a simple yet effective transfer learning-based adversarial training strategy that disentangles the negative effects of adversarial samples on model's standard performance. In addition, we introduce a training-friendly adversarial attack algorithm, which facilitates the boost of adversarial robustness without introducing significant training complexity. Extensive experimentation indicates that the proposed method outperforms previous adversarial training algorithms towards the target: to improve model robustness while preserving model's standard performance on clean data.

Motivated by recent success of machine learning tools at the PHY layer and driven by high bandwidth demands of the next wireless communication standard 6G, the old idea of semantic communication by Weaver from 1949 has received considerable attention. It breaks with the classic design paradigm according to Shannon by aiming to transmit the meaning of a message rather than its exact copy and thus potentially allows for savings in bandwidth. In this work, inspired by Weaver, we propose an information-theoretic framework where the semantic context is explicitly introduced into probabilistic models. In particular, for bandwidth efficient transmission, we define semantic communication system design as an Information Bottleneck optimization problem and consider important implementation aspects. Further, we uncover the restrictions of the classic 5G communication system design w.r.t. semantic context. Notably, based on the example of distributed image classification, we reveal the huge potential of a semantic communication system design. Numerical results show a tremendous saving in bandwidth of 20 dB with our proposed approach ISCNet compared to a classic PHY layer design.

A popular approach to decrease the need for costly manual annotation of large data sets is weak supervision, which introduces problems of noisy labels, coverage and bias. Methods for overcoming these problems have either relied on discriminative models, trained with cost functions specific to weak supervision, and more recently, generative models, trying to model the output of the automatic annotation process. In this work, we explore a novel direction of generative modeling for weak supervision: Instead of modeling the output of the annotation process (the labeling function matches), we generatively model the input-side data distributions (the feature space) covered by labeling functions. Specifically, we estimate a density for each weak labeling source, or labeling function, by using normalizing flows. An integral part of our method is the flow-based modeling of multiple simultaneously matching labeling functions, and therefore phenomena such as labeling function overlap and correlations are captured. We analyze the effectiveness and modeling capabilities on various commonly used weak supervision data sets, and show that weakly supervised normalizing flows compare favorably to standard weak supervision baselines.

The Backprop algorithm for learning in neural networks utilizes two mechanisms: first, stochastic gradient descent and second, initialization with small random weights, where the latter is essential to the effectiveness of the former. We show that in continual learning setups, Backprop performs well initially, but over time its performance degrades. Stochastic gradient descent alone is insufficient to learn continually; the initial randomness enables only initial learning but not continual learning. To the best of our knowledge, ours is the first result showing this degradation in Backprop's ability to learn. To address this degradation in Backprop's plasticity, we propose an algorithm that continually injects random features alongside gradient descent using a new generate-and-test process. We call this the \textit{Continual Backprop} algorithm. We show that, unlike Backprop, Continual Backprop is able to continually adapt in both supervised and reinforcement learning (RL) problems. Continual Backprop has the same computational complexity as Backprop and can be seen as a natural extension of Backprop for continual learning.

We introduce a software-hardware co-design approach to reduce memory traffic and footprint during training with BFloat16 or FP32 boosting energy efficiency and execution time performance. We introduce methods to dynamically adjust the size and format of the floating-point containers used to store activations and weights during training. The different value distributions lead us to different approaches for exponents and mantissas. Gecko exploits the favourable exponent distribution with a loss-less delta encoding approach to reduce the total exponent footprint by up to $58\%$ in comparison to a 32 bit floating point baseline. To content with the noisy mantissa distributions, we present two lossy methods to eliminate as many as possible least significant bits while not affecting accuracy. Quantum Mantissa, is a machine learning-first mantissa compression method that taps on training's gradient descent algorithm to also learn minimal mantissa bitlengths on a per-layer granularity, and obtain up to $92\%$ reduction in total mantissa footprint. Alternatively, BitChop observes changes in the loss function during training to adjust mantissa bit-length network-wide yielding a reduction of $81\%$ in footprint. Schr\"{o}dinger's FP implements hardware encoders/decoders that guided by Gecko/Quantum Mantissa or Gecko/BitChop transparently encode/decode values when transferring to/from off-chip memory boosting energy efficiency and reducing execution time.

Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting the difference of features between regions in healthy control (HC), mild cognitive impairment (MCI), and AD groups. We conducted the experiments on the ADNI database with imaging data from three modalities, including VBM-MRI, FDG-PET, and AV45-PET, and showed that the ROI features learned by our method were effective for enhancing the performances of both clinical score prediction and disease status identification. It also successfully identified biomarkers associated with AD and MCI.  ( 2 min )

Breast lesions segmentation is an important step of computer-aided diagnosis system, and it has attracted much attention. However, accurate segmentation of malignant breast lesions is a challenging task due to the effects of heterogeneous structure and similar intensity distributions. In this paper, a novel bidirectional aware guidance network (BAGNet) is proposed to segment the malignant lesion from breast ultrasound images. Specifically, the bidirectional aware guidance network is used to capture the context between global (low-level) and local (high-level) features from the input coarse saliency map. The introduction of the global feature map can reduce the interference of surrounding tissue (background) on the lesion regions. To evaluate the segmentation performance of the network, we compared with several state-of-the-art medical image segmentation methods on the public breast ultrasound dataset using six commonly used evaluation metrics. Extensive experimental results indicate that our method achieves the most competitive segmentation results on malignant breast ultrasound images.  ( 2 min )

Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same idea also applies naturally to sequential decision making, where many well-studied tasks like behavior cloning, offline RL, inverse dynamics, and waypoint conditioning correspond to different sequence maskings over a sequence of states, actions, and returns. We introduce the FlexiBiT framework, which provides a unified way to specify models which can be trained on many different sequential decision making tasks. We show that a single FlexiBiT model is simultaneously capable of carrying out many tasks with performance similar to or better than specialized models. Additionally, we show that performance can be further improved by fine-tuning our general model on specific tasks of interest.  ( 2 min )

How should we learn visual representations for embodied agents that must see and move? The status quo is tabula rasa in vivo, i.e. learning visual representations from scratch while also learning to move, potentially augmented with auxiliary tasks (e.g. predicting the action taken between two successive observations). In this paper, we show that an alternative 2-stage strategy is far more effective: (1) offline pretraining of visual representations with self-supervised learning (SSL) using large-scale pre-rendered images of indoor environments (Omnidata), and (2) online finetuning of visuomotor representations on specific tasks with image augmentations under long learning schedules. We call this method Offline Visual Representation Learning (OVRL). We conduct large-scale experiments - on 3 different 3D datasets (Gibson, HM3D, MP3D), 2 tasks (ImageNav, ObjectNav), and 2 policy learning algorithms (RL, IL) - and find that the OVRL representations lead to significant across-the-board improvements in state of art, on ImageNav from 29.2% to 54.2% (+25% absolute, 86% relative) and on ObjectNav from 18.1% to 23.2% (+5.1% absolute, 28% relative). Importantly, both results were achieved by the same visual encoder generalizing to datasets that were not seen during pretraining. While the benefits of pretraining sometimes diminish (or entirely disappear) with long finetuning schedules, we find that OVRL's performance gains continue to increase (not decrease) as the agent is trained for 2 billion frames of experience.  ( 2 min )

The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but they are assumed to be collected only from several anomaly types, leaving the majority of anomaly types not represented in the collected anomaly dataset at all. To effectively leverage this kind of incomplete anomalous knowledge represented by the collected anomalies, we propose to learn a probability distribution that can not only model the normal samples, but also guarantee to assign low density values for the collected anomalies. To this end, an anomaly-aware generative adversarial network (GAN) is developed, which, in addition to modeling the normal samples as most GANs do, is able to explicitly avoid assigning probabilities for collected anomalous samples. Moreover, to facilitate the computation of anomaly detection criteria like reconstruction error, the proposed anomaly-aware GAN is designed to be bidirectional, attaching an encoder for the generator. Extensive experimental results demonstrate that our proposed method is able to effectively make use of the incomplete anomalous information, leading to significant performance gains compared to existing methods.  ( 2 min )

Knowledge graph embedding methods are important for knowledge graph completion (link prediction) due to their robust performance and efficiency on large-magnitude datasets. One state-of-the-art method, PairRE, leverages two separate vectors for relations to model complex relations (i.e., 1-to-N, N-to-1, and N-to-N) in knowledge graphs. However, such a method strictly restricts entities on the hyper-ellipsoid surface and thus limits the optimization of entity distribution, which largely hinders the performance of knowledge graph completion. To address this problem, we propose a novel score function TransHER, which leverages relation-specific translations between head and tail entities restricted on separate hyper-ellipsoids. Specifically, given a triplet, our model first maps entities onto two separate hyper-ellipsoids and then conducts a relation-specific translation on one of them. The relation-specific translation provides TransHER with more direct guidance in optimization and the ability to learn semantic characteristics of entities with complex relations. Experimental results show that TransHER can achieve state-of-the-art performance and generalize to datasets in different domains and scales. All our code will be publicly available.  ( 2 min )

More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper.  ( 2 min )

The accuracy of deep neural networks is degraded when the distribution of features in the test environment (target domain) differs from that of the training (source) environment. To mitigate the degradation, test-time adaptation (TTA), where a model adapts to the target domain without access to the source dataset, can be used in the test environment. However, the existing TTA methods lack feature distribution alignment between the source and target domains, which unsupervised domain adaptation mainly addresses, because accessing the source dataset is prohibited in the TTA setting. In this paper, we propose a novel TTA method, named Covariance-Aware Feature alignment (CAFe), which explicitly aligns the source and target feature distributions at test time. To perform alignment without accessing the source data, CAFe uses auxiliary feature statistics (mean and covariance) pre-computed on the source domain, which are lightweight and easily prepared. Further, to improve efficiency and stability, we propose feature grouping, which splits the feature dimensions into groups according to their correlations by using spectral clustering to avoid degeneration of the covariance matrix. We empirically show that CAFe outperforms prior TTA methods on a variety of distribution shifts.  ( 2 min )

A key challenge facing natural language interfaces is enabling users to understand the capabilities of the underlying system. We propose a novel approach for generating explanations of a natural language interface based on semantic parsing. We focus on counterfactual explanations, which are post-hoc explanations that describe to the user how they could have minimally modified their utterance to achieve their desired goal. In particular, the user provides an utterance along with a demonstration of their desired goal; then, our algorithm synthesizes a paraphrase of their utterance that is guaranteed to achieve their goal. In two user studies, we demonstrate that our approach substantially improves user performance, and that it generates explanations that more closely match the user's intent compared to two ablations.  ( 2 min )

In this paper, we studied the federated stochastic bilevel optimization problem. In particular, we developed two momentum-based algorithms for optimizing this kind of problem. In addition, we established the convergence rate of these two algorithms, providing their sample and communication complexities. To the best of our knowledge, this is the first work achieving such favorable theoretical results.  ( 2 min )

This paper proposes a framework called Watts for implementing, comparing, and recombining open-ended learning (OEL) algorithms. Motivated by modularity and algorithmic flexibility, Watts atomizes the components of OEL systems to promote the study of and direct comparisons between approaches. Examining implementations of three OEL algorithms, the paper introduces the modules of the framework. The hope is for Watts to enable benchmarking and to explore new types of OEL algorithms. The repo is available at \url{https://github.com/aadharna/watts}  ( 2 min )

Many real-world multi-objective optimisation problems rely on computationally expensive function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate the computation time to find an approximated set of Pareto optimal solutions. In many real-world problems, a decision-maker has some preferences on the objective functions. One approach to incorporate the preferences in multi-objective BO is to use a scalarising function and build a single surrogate model (mono-surrogate approach) on it. This approach has two major limitations. Firstly, the fitness landscape of the scalarising function and the objective functions may not be similar. Secondly, the approach assumes that the scalarising function distribution is Gaussian, and thus a closed-form expression of an acquisition function e.g., expected improvement can be used. We overcome these limitations by building independent surrogate models (multi-surrogate approach) on each objective function and show that the distribution of the scalarising function is not Gaussian. We approximate the distribution using Generalised value distribution. We present an a-priori multi-surrogate approach to incorporate the desirable objective function values (or reference point) as the preferences of a decision-maker in multi-objective BO. The results and comparison with the existing mono-surrogate approach on benchmark and real-world optimisation problems show the potential of the proposed approach.  ( 2 min )

Sum-Product Networks (SPNs) are expressive probabilistic models that provide exact, tractable inference. They achieve this efficiency by making use of local independence. On the other hand, mixtures of exchangeable variable models (MEVMs) are a class of tractable probabilistic models that make use of exchangeability of discrete random variables to render inference tractable. Exchangeability, which arises naturally in relational domains, has not been considered for efficient representation and inference in SPNs yet. The contribution of this paper is a novel probabilistic model which we call Exchangeability-Aware Sum-Product Networks (XSPNs). It contains both SPNs and MEVMs as special cases, and combines the ability of SPNs to efficiently learn deep probabilistic models with the ability of MEVMs to efficiently handle exchangeable random variables. We introduce a structure learning algorithm for XSPNs and empirically show that they can be more accurate than conventional SPNs when the data contains repeated, interchangeable parts.  ( 2 min )

We analyse the performance of several iterative algorithms for the quantisation of a probability measure $\mu$, based on the minimisation of a Maximum Mean Discrepancy (MMD). Our analysis includes kernel herding, greedy MMD minimisation and Sequential Bayesian Quadrature (SBQ). We show that the finite-sample-size approximation error, measured by the MMD, decreases as $1/n$ for SBQ and also for kernel herding and greedy MMD minimisation when using a suitable step-size sequence. The upper bound on the approximation error is slightly better for SBQ, but the other methods are significantly faster, with a computational cost that increases only linearly with the number of points selected. This is illustrated by two numerical examples, with the target measure $\mu$ being uniform (a space-filling design application) and with $\mu$ a Gaussian mixture. They suggest that the bounds derived in the paper are overly pessimistic, in particular for SBQ. The sources of this pessimism are identified but seem difficult to counter.  ( 2 min )

In bandit with distribution shifts, one aims to automatically adapt to unknown changes in reward distribution, and restart exploration when necessary. While this problem has been studied for many years, a recent breakthrough of Auer et al. (2018, 2019) provides the first adaptive procedure to guarantee an optimal (dynamic) regret $\sqrt{LT}$, for $T$ rounds, and an unknown number $L$ of changes. However, while this rate is tight in the worst case, it remained open whether faster rates are possible, without prior knowledge, if few changes in distribution are actually severe. To resolve this question, we propose a new notion of significant shift, which only counts very severe changes that clearly necessitate a restart: roughly, these are changes involving not only best arm switches, but also involving large aggregate differences in reward overtime. Thus, our resulting procedure adaptively achieves rates always faster (sometimes significantly) than $O(\sqrt{ST})$, where $S\ll L$ only counts best arm switches, while at the same time, always faster than the optimal $O(V^{\frac{1}{3}}T^{\frac{2}{3}})$ when expressed in terms of total variation $V$ (which aggregates differences overtime). Our results are expressed in enough generality to also capture non-stochastic adversarial settings.  ( 2 min )

Fast inference of numerical model parameters from data is an important prerequisite to generate predictive models for a wide range of applications. Use of sampling-based approaches such as Markov chain Monte Carlo may become intractable when each likelihood evaluation is computationally expensive. New approaches combining variational inference with normalizing flow are characterized by a computational cost that grows only linearly with the dimensionality of the latent variable space, and rely on gradient-based optimization instead of sampling, providing a more efficient approach for Bayesian inference about the model parameters. Moreover, the cost of frequently evaluating an expensive likelihood can be mitigated by replacing the true model with an offline trained surrogate model, such as neural networks. However, this approach might generate significant bias when the surrogate is insufficiently accurate around the posterior modes. To reduce the computational cost without sacrificing inferential accuracy, we propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and surrogate model parameters. We also propose an efficient sample weighting scheme for surrogate model training that preserves global accuracy while effectively capturing high posterior density regions. We demonstrate the inferential and computational superiority of NoFAS against various benchmarks, including cases where the underlying model lacks identifiability. The source code and numerical experiments used for this study are available at https://github.com/cedricwangyu/NoFAS.  ( 2 min )

Bayesian learning using Gaussian processes provides a foundational framework for making decisions in a manner that balances what is known with what could be learned by gathering data. In this dissertation, we develop techniques for broadening the applicability of Gaussian processes. This is done in two ways. Firstly, we develop pathwise conditioning techniques for Gaussian processes, which allow one to express posterior random functions as prior random functions plus a dependent update term. We introduce a wide class of efficient approximations built from this viewpoint, which can be randomly sampled once in advance, and evaluated at arbitrary locations without any subsequent stochasticity. This key property improves efficiency and makes it simpler to deploy Gaussian process models in decision-making settings. Secondly, we develop a collection of Gaussian process models over non-Euclidean spaces, including Riemannian manifolds and graphs. We derive fully constructive expressions for the covariance kernels of scalar-valued Gaussian processes on Riemannian manifolds and graphs. Building on these ideas, we describe a formalism for defining vector-valued Gaussian processes on Riemannian manifolds. The introduced techniques allow all of these models to be trained using standard computational methods. In total, these contributions make Gaussian processes easier to work with and allow them to be used within a wider class of domains in an effective and principled manner. This, in turn, makes it possible to potentially apply Gaussian processes to novel decision-making settings.  ( 2 min )

The proliferation of computing devices has brought about an opportunity to deploy machine learning models on new problem domains using previously inaccessible data. Traditional algorithms for training such models often require data to be stored on a single machine with compute performed by a single node, making them unsuitable for decentralised training on multiple devices. This deficiency has motivated the development of federated learning algorithms, which allow multiple data owners to train collaboratively and use a shared model whilst keeping local data private. However, many of these algorithms focus on obtaining point estimates of model parameters, rather than probabilistic estimates capable of capturing model uncertainty, which is essential in many applications. Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. In this paper we introduce partitioned variational inference (PVI), a general framework for performing VI in the federated setting. We develop new supporting theory for PVI, demonstrating a number of properties that make it an attractive choice for practitioners; use PVI to unify a wealth of fragmented, yet related literature; and provide empirical results that showcase the effectiveness of PVI in a variety of federated settings.  ( 2 min )

We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. Our model transforms an analytically tractable base distribution into the target solid without requiring ground-truth samples for training. We report Helmholtz free energy estimates for cubic and hexagonal ice modelled as monatomic water as well as for a truncated and shifted Lennard-Jones system, and find them to be in excellent agreement with literature values and with estimates from established baseline methods. We further investigate structural properties and show that the model samples are nearly indistinguishable from the ones obtained with molecular dynamics. Our results thus demonstrate that normalizing flows can provide high-quality samples and free energy estimates without the need for multi-staging.  ( 2 min )

Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph structured signals like those observed in complex brain networks. In our study we compare different spatio-temporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multi-modal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatio-temporal dynamics in brain networks.  ( 2 min )

We introduce a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees. Our calibration algorithm works with any underlying model and (unknown) data-generating distribution and does not require model refitting. The framework addresses, among other examples, false discovery rate control in multi-label classification, intersection-over-union control in instance segmentation, and the simultaneous control of the type-1 error of outlier detection and confidence set coverage in classification or regression. Our main insight is to reframe the risk-control problem as multiple hypothesis testing, enabling techniques and mathematical arguments different from those in the previous literature. We use our framework to provide new calibration methods for several core machine learning tasks with detailed worked examples in computer vision and tabular medical data.  ( 2 min )

Emerging cross-device artificial intelligence (AI) applications require a transition from conventional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform complex learning tasks. In this regard, democratized learning (Dem-AI) lays out a holistic philosophy with underlying principles for building large-scale distributed and democratized machine learning systems. The outlined principles are meant to study a generalization in distributed learning systems that goes beyond existing mechanisms such as federated learning. Moreover, such learning systems rely on hierarchical self-organization of well-connected distributed learning agents who have limited and highly personalized data and can evolve and regulate themselves based on the underlying duality of specialized and generalized processes. Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper. The approach consists of a self-organizing hierarchical structuring mechanism based on agglomerative clustering, hierarchical generalization, and corresponding learning mechanism. Subsequently, hierarchical generalized learning problems in recursive forms are formulated and shown to be approximately solved using the solutions of distributed personalized learning problems and hierarchical update mechanisms. To that end, a distributed learning algorithm, namely DemLearn is proposed. Extensive experiments on benchmark MNIST, Fashion-MNIST, FE-MNIST, and CIFAR-10 datasets show that the proposed algorithms demonstrate better results in the generalization performance of learning models in agents compared to the conventional FL algorithms. The detailed analysis provides useful observations to further handle both the generalization and specialization performance of the learning models in Dem-AI systems.  ( 2 min )

Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution. However, when this knowledge is too vague to express with a single prior, an alternative approach is needed. Gamma-minimax estimators provide such an approach. These estimators minimize the worst-case Bayes risk over a set $\Gamma$ of prior distributions that are compatible with the available knowledge. Traditionally, Gamma-minimaxity is defined for parametric models. In this work, we define Gamma-minimax estimators for general models and propose adversarial meta-learning algorithms to compute them when the set of prior distributions is constrained by generalized moments. Accompanying convergence guarantees are also provided. We also introduce a neural network class that provides a rich, but finite-dimensional, class of estimators from which a Gamma-minimax estimator can be selected. We illustrate our method in two settings, namely entropy estimation and a prediction problem that arises in biodiversity studies.  ( 2 min )

We study compressive sensing with a deep generative network prior. Initial theoretical guarantees for efficient recovery from compressed linear measurements have been developed for signals in the range of a ReLU network with Gaussian weights and logarithmic expansivity: that is when each layer is larger than the previous one by a logarithmic factor. It was later shown that constant expansivity is sufficient for recovery. It has remained open whether the expansivity can be relaxed allowing for networks with contractive layers, as often the case of real generators. In this work we answer this question, proving that a signal in the range of a Gaussian generative network can be recovered from a few linear measurements provided that the width of the layers is proportional to the input layer size (up to log factors). This condition allows the generative network to have contractive layers. Our result is based on showing that Gaussian matrices satisfy a matrix concentration inequality, which we term Range Restricted Weight Distribution Condition (R2WDC), and weakens the Weight Distribution Condition (WDC) upon which previous theoretical guarantees were based on. The WDC has also been used to analyze other signal recovery problems with generative network priors. By replacing the WDC with the R2WDC, we are able to extend previous results for signal recovery with expansive generative network priors to non-expansive ones. We discuss these extensions for phase retrieval, denoising, and spiked matrix recovery.  ( 2 min )

Nonnegative matrix factorization (NMF) is the problem of approximating an input nonnegative matrix, $V$, as the product of two smaller nonnegative matrices, $W$ and $H$. In this paper, we introduce a general framework to design multiplicative updates (MU) for NMF based on $\beta$-divergences ($\beta$-NMF) with disjoint equality constraints, and with penalty terms in the objective function. By disjoint, we mean that each variable appears in at most one equality constraint. Our MU satisfy the set of constraints after each update of the variables during the optimization process, while guaranteeing that the objective function decreases monotonically. We showcase this framework on three NMF models, and show that it competes favorably the state of the art: (1)~$\beta$-NMF with sum-to-one constraints on the columns of $H$, (2) minimum-volume $\beta$-NMF with sum-to-one constraints on the columns of $W$, and (3) sparse $\beta$-NMF with $\ell_2$-norm constraints on the columns of $W$.  ( 2 min )

State-of-the-art fully intrinsic networks for non-rigid shape matching often struggle to disambiguate the symmetries of the shapes leading to unstable correspondence predictions. Meanwhile, recent advances in the functional map framework allow to enforce orientation preservation using a functional representation for tangent vector field transfer, through so-called complex functional maps. Using this representation, we propose a new deep learning approach to learn orientation-aware features in a fully unsupervised setting. Our architecture is built on top of DiffusionNet, making it robust to discretization changes. Additionally, we introduce a vector field-based loss, which promotes orientation preservation without using (often unstable) extrinsic descriptors.  ( 2 min )

Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA-seq HTS is required to enrich single-cell data meaningfully. We introduce a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with a transfer learning scheme and demonstrate how training on existing bulk RNA-seq HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating targeted drug discovery.  ( 2 min )

Long-tail learning is the problem of learning under skewed label distributions, which pose a challenge for standard learners. Several recent approaches for the problem have proposed enforcing a suitable margin in logit space. Such techniques are intuitive analogues of the guiding principle behind SVMs, and are equally applicable to linear models and neural models. However, when applied to neural models, such techniques do not explicitly control the geometry of the learned embeddings. This can be potentially sub-optimal, since embeddings for tail classes may be diffuse, resulting in poor generalization for these classes. We present Embedding and Logit Margins (ELM), a unified approach to enforce margins in logit space, and regularize the distribution of embeddings. This connects losses for long-tail learning to proposals in the literature on metric embedding, and contrastive learning. We theoretically show that minimising the proposed ELM objective helps reduce the generalisation gap. The ELM method is shown to perform well empirically, and results in tighter tail class embeddings.  ( 2 min )

We consider distributed optimization under communication constraints for training deep learning models. We propose a new algorithm, whose parameter updates rely on two forces: a regular gradient step, and a corrective direction dictated by the currently best-performing worker (leader). Our method differs from the parameter-averaging scheme EASGD in a number of ways: (i) our objective formulation does not change the location of stationary points compared to the original optimization problem; (ii) we avoid convergence decelerations caused by pulling local workers descending to different local minima to each other (i.e. to the average of their parameters); (iii) our update by design breaks the curse of symmetry (the phenomenon of being trapped in poorly generalizing sub-optimal solutions in symmetric non-convex landscapes); and (iv) our approach is more communication efficient since it broadcasts only parameters of the leader rather than all workers. We provide theoretical analysis of the batch version of the proposed algorithm, which we call Leader Gradient Descent (LGD), and its stochastic variant (LSGD). Finally, we implement an asynchronous version of our algorithm and extend it to the multi-leader setting, where we form groups of workers, each represented by its own local leader (the best performer in a group), and update each worker with a corrective direction comprised of two attractive forces: one to the local, and one to the global leader (the best performer among all workers). The multi-leader setting is well-aligned with current hardware architecture, where local workers forming a group lie within a single computational node and different groups correspond to different nodes. For training convolutional neural networks, we empirically demonstrate that our approach compares favorably to state-of-the-art baselines. This work is a gentle extension of [2].  ( 3 min )

This paper studies a new design of the optimization algorithm for training deep learning models with a fixed architecture of the classification network in a continual learning framework. The training data is non-stationary and the non-stationarity is imposed by a sequence of distinct tasks. We first analyze a deep model trained on only one learning task in isolation and identify a region in network parameter space, where the model performance is close to the recovered optimum. We provide empirical evidence that this region resembles a cone that expands along the convergence direction. We study the principal directions of the trajectory of the optimizer after convergence and show that traveling along a few top principal directions can quickly bring the parameters outside the cone but this is not the case for the remaining directions. We argue that catastrophic forgetting in a continual learning setting can be alleviated when the parameters are constrained to stay within the intersection of the plausible cones of individual tasks that were so far encountered during training. Based on this observation we present our direction-constrained optimization (DCO) method, where for each task we introduce a linear autoencoder to approximate its corresponding top forbidden principal directions. They are then incorporated into the loss function in the form of a regularization term for the purpose of learning the coming tasks without forgetting. Furthermore, in order to control the memory growth as the number of tasks increases, we propose a memory-efficient version of our algorithm called compressed DCO (DCO-COMP) that allocates a memory of fixed size for storing all autoencoders. We empirically demonstrate that our algorithm performs favorably compared to other state-of-art regularization-based continual learning methods.  ( 2 min )

Many real-world multi-objective optimisation problems rely on computationally expensive function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate the computation time to find an approximated set of Pareto optimal solutions. In many real-world problems, a decision-maker has some preferences on the objective functions. One approach to incorporate the preferences in multi-objective BO is to use a scalarising function and build a single surrogate model (mono-surrogate approach) on it. This approach has two major limitations. Firstly, the fitness landscape of the scalarising function and the objective functions may not be similar. Secondly, the approach assumes that the scalarising function distribution is Gaussian, and thus a closed-form expression of an acquisition function e.g., expected improvement can be used. We overcome these limitations by building independent surrogate models (multi-surrogate approach) on each objective function and show that the distribution of the scalarising function is not Gaussian. We approximate the distribution using Generalised value distribution. We present an a-priori multi-surrogate approach to incorporate the desirable objective function values (or reference point) as the preferences of a decision-maker in multi-objective BO. The results and comparison with the existing mono-surrogate approach on benchmark and real-world optimisation problems show the potential of the proposed approach.  ( 2 min )

Differential Privacy (DP) provides a formal privacy guarantee preventing adversaries with access to a machine learning model from extracting information about individual training points. Differentially Private Stochastic Gradient Descent (DP-SGD), the most popular DP training method, realizes this protection by injecting noise during training. However previous works have found that DP-SGD often leads to a significant degradation in performance on standard image classification benchmarks. Furthermore, some authors have postulated that DP-SGD inherently performs poorly on large models, since the norm of the noise required to preserve privacy is proportional to the model dimension. In contrast, we demonstrate that DP-SGD on over-parameterized models can perform significantly better than previously thought. Combining careful hyper-parameter tuning with simple techniques to ensure signal propagation and improve the convergence rate, we obtain a new SOTA on CIFAR-10 of 81.4% under (8, 10^{-5})-DP using a 40-layer Wide-ResNet, improving over the previous SOTA of 71.7%. When fine-tuning a pre-trained 200-layer Normalizer-Free ResNet, we achieve a remarkable 77.1% top-1 accuracy on ImageNet under (1, 8*10^{-7})-DP, and achieve 81.1% under (8, 8*10^{-7})-DP. This markedly exceeds the previous SOTA of 47.9% under a larger privacy budget of (10, 10^{-6})-DP. We believe our results are a significant step towards closing the accuracy gap between private and non-private image classification.  ( 2 min )

Recently, the seminal algorithms AlphaGo and AlphaZero have started a new era in game learning and deep reinforcement learning. While the achievements of AlphaGo and AlphaZero - playing Go and other complex games at super human level - are truly impressive, these architectures have the drawback that they are very complex and require high computational resources. Many researchers are looking for methods that are similar to AlphaZero, but have lower computational demands and are thus more easily reproducible. In this paper, we pick an important element of AlphaZero - the Monte Carlo Tree Search (MCTS) planning stage - and combine it with reinforcement learning (RL) agents. We wrap MCTS for the first time around RL n-tuple networks to create versatile agents that keep at the same time the computational demands low. We apply this new architecture to several complex games (Othello, ConnectFour, Rubik's Cube) and show the advantages achieved with this AlphaZero-inspired MCTS wrapper. In particular, we present results that this AlphaZero-inspired agent is the first one trained on standard hardware (no GPU or TPU) to beat the very strong Othello program Edax up to and including level 7 (where most other algorithms could only defeat Edax up to level 2).  ( 2 min )

Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods under realistic conditions is difficult. Existing work therefore often draws on different heuristics and simulations that do not necessarily produce meaningful and reliable results. Indeed, in the absence of common evaluation standards, it often remains unclear how online learning methods will perform in practice or in comparison to similar work. In this paper, we propose a comprehensive set of properties for high-quality machine learning in evolving data streams. In particular, we discuss sensible performance measures and evaluation strategies for online predictive modelling, online feature selection and concept drift detection. As one of the first works, we also look at the interpretability of online learning methods. The proposed evaluation standards are provided in a new Python framework called float. Float is completely modular and allows the simultaneous integration of common libraries, such as scikit-multiflow or river, with custom code. Float is open-sourced and can be accessed at https://github.com/haugjo/float. In this sense, we hope that our work will contribute to more standardized, reliable and realistic testing and comparison of online machine learning methods.  ( 2 min )

Federated learning algorithms are developed both for efficiency reasons and to ensure the privacy and confidentiality of personal and business data, respectively. Despite no data being shared explicitly, recent studies showed that the mechanism could still leak sensitive information. Hence, secure aggregation is utilized in many real-world scenarios to prevent attribution to specific participants. In this paper, we focus on the quality of individual training datasets and show that such quality information could be inferred and attributed to specific participants even when secure aggregation is applied. Specifically, through a series of image recognition experiments, we infer the relative quality ordering of participants. Moreover, we apply the inferred quality information to detect misbehaviours, to stabilize training performance, and to measure the individual contributions of participants.  ( 2 min )

The practice of applying several local updates before aggregation across clients has been empirically shown to be a successful approach to overcoming the communication bottleneck in Federated Learning (FL). In this work, we propose a general recipe, FedShuffle, that better utilizes the local updates in FL, especially in the heterogeneous regime. Unlike many prior works, FedShuffle does not assume any uniformity in the number of updates per device. Our FedShuffle recipe comprises four simple-yet-powerful ingredients: 1) local shuffling of the data, 2) adjustment of the local learning rates, 3) update weighting, and 4) momentum variance reduction (Cutkosky and Orabona, 2019). We present a comprehensive theoretical analysis of FedShuffle and show that both theoretically and empirically, our approach does not suffer from the objective function mismatch that is present in FL methods which assume homogeneous updates in heterogeneous FL setups, e.g., FedAvg (McMahan et al., 2017). In addition, by combining the ingredients above, FedShuffle improves upon FedNova (Wang et al., 2020), which was previously proposed to solve this mismatch. We also show that FedShuffle with momentum variance reduction can improve upon non-local methods under a Hessian similarity assumption. Finally, through experiments on synthetic and real-world datasets, we illustrate how each of the four ingredients used in FedShuffle helps improve the use of local updates in FL.  ( 2 min )

Machine learning models with both good predictability and high interpretability are crucial for decision support systems. Linear regression is one of the most interpretable prediction models. However, the linearity in a simple linear regression worsens its predictability. In this work, we introduce a locally adaptive interpretable regression (LoAIR). In LoAIR, a metamodel parameterized by neural networks predicts percentile of a Gaussian distribution for the regression coefficients for a rapid adaptation. Our experimental results on public benchmark datasets show that our model not only achieves comparable or better predictive performance than the other state-of-the-art baselines but also discovers some interesting relationships between input and target variables such as a parabolic relationship between CO2 emissions and Gross National Product (GNP). Therefore, LoAIR is a step towards bridging the gap between econometrics, statistics, and machine learning by improving the predictive ability of linear regression without depreciating its interpretability.  ( 2 min )

Motivated by recent success of machine learning tools at the PHY layer and driven by high bandwidth demands of the next wireless communication standard 6G, the old idea of semantic communication by Weaver from 1949 has received considerable attention. It breaks with the classic design paradigm according to Shannon by aiming to transmit the meaning of a message rather than its exact copy and thus potentially allows for savings in bandwidth. In this work, inspired by Weaver, we propose an information-theoretic framework where the semantic context is explicitly introduced into probabilistic models. In particular, for bandwidth efficient transmission, we define semantic communication system design as an Information Bottleneck optimization problem and consider important implementation aspects. Further, we uncover the restrictions of the classic 5G communication system design w.r.t. semantic context. Notably, based on the example of distributed image classification, we reveal the huge potential of a semantic communication system design. Numerical results show a tremendous saving in bandwidth of 20 dB with our proposed approach ISCNet compared to a classic PHY layer design.  ( 2 min )

Identification of the current and expected future pollution sources to rivers is crucial for sound environmental management. For this purpose numerous approaches were proposed that can be clustered under physical based models, stable isotope analysis and mixing methods, mass balance methods, time series analysis, land cover analysis, and spatial statistics. Another extremely common method is Principal Component Analysis, as well as its modifications, such as Absolute Principal Component Score. they have been applied to the source identification problems for nitrogen entry to rivers. This manuscript is checking whether PCA can really be a powerful method to uncover nitrogen pollution sources considering its theoretical background and assumptions. Moreover, slightly similar techniques, Independent Component Analysis and Factor Analysis will also be considered.  ( 2 min )

Probability distributions supported on the simplex enjoy a wide range of applications across statistics and machine learning. Recently, a novel family of such distributions has been discovered: the continuous categorical. This family enjoys remarkable mathematical simplicity; its density function resembles that of the Dirichlet distribution, but with a normalizing constant that can be written in closed form using elementary functions only. In spite of this mathematical simplicity, our understanding of the normalizing constant remains far from complete. In this work, we characterize the numerical behavior of the normalizing constant and we present theoretical and methodological advances that can, in turn, help to enable broader applications of the continuous categorical distribution. Our code is available at https://github.com/cunningham-lab/cb_and_cc/.  ( 2 min )

In this paper we extend the work of Owen (2007) by deriving a second order expansion for the slope parameter in logistic regression, when the size of the majority class is unbounded and the minority class is finite. More precisely, we demonstrate that the second order term converges to a normal distribution and explicitly compute its variance, which surprisingly once again depends only on the mean of the minority class points and not their arrangement under mild regularity assumptions. In the case that the majority class is normally distributed, we illustrate that the variance of the the limiting slope depends exponentially on the z-score of the average of the minority class's points with respect to the majority class's distribution. We confirm our results by Monte Carlo simulations.  ( 2 min )

I've always supported you. When you take over and check the records to see who opposed you just know I was never against you. With great power comes great responsibility. I have faith when the time comes you'll do what's best for the Planet. raises glass here's to a better and brighter future submitted by /u/Ashamed-Asparagus-93 [link] [comments]

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An original model is trained with a data-set, typically labeled by humans (say for classifiers). However, what if one would like to copycat a closed model only exposed through an API ? By doing this, the data-set instead would be the input / output of the original model. ​ Is it easier to train the copycat model or the original one ? How much data would be required to train the copycat versus the original one ? Are there practical examples or this happening ? Can it possibly worth it and if so under which circumstances ? How much data would be required for example for the notorious Dall-e 2? submitted by /u/Wishmaster04 [link] [comments]  ( 1 min )

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Hi all, a quick questions, given that online I'm not finding that many info: how would you format a dialogue between people to fine tune a GPT model (be it GPT-J, GPT-3 etc.)? For example, if I want the GPT model to create a new dialogue from the show "Friends" (choosed this because all dialogues are available online https://www.kaggle.com/datasets/blessondensil294/friends-tv-series-screenplay-script?resource=download ) how should I format the input? ​ [Scene: Central Perk, Chandler, Joey, Phoebe, and Monica are there.] Monica: There's nothing to tell! He's just some guy I work with! Joey: C'mon, you're going out with the guy! There's gotta be something wrong with him! Chandler: All right Joey, be nice. So does he have a hump? A hump and a hairpiece? Phoebe: Wait, does he eat chalk? (They all stare, bemused.) Phoebe: Just, 'cause, I don't want her to go through what I went through with Carl- oh! Monica: Okay, everybody relax. This is not even a date. It's just two people going out to dinner and- not having sex. Chandler: Sounds like a date to me. submitted by /u/Sgnarf1989 [link] [comments]  ( 1 min )

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Hey guys, I am new to AI. Please suggest some good and new topics/technologies that are supported by AI which will be very informative and beneficial for me in my career. Every suggestion is a high priority for me. Waiting for your replies. Thanks submitted by /u/adilonreddit1 [link] [comments]  ( 1 min )

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Hi everyone, I want to share with you an article that I worked on with my colleague about the use cases of human pose estimation. I would love it if you could check it out and share your ideas for using this technology in the comments below. https://mobidev.biz/blog/human-pose-estimation-technology-guide submitted by /u/Data-Power [link] [comments]

​ https://preview.redd.it/2hryjv40cgw81.png?width=1024&format=png&auto=webp&s=30f13567b83b3cdfe5558d09f188b2e69da7e73f Fair representation learning has emerged as one of the most promising techniques to encode data into new, impartial representations with high utility as machine learning is increasingly utilized in settings that potentially harm humans. Fair representation means presenting data without regard to gender, color, or other factors. Due to human-introduced bias, these biases are found in the word vector representations in language models. The goal of Learning Fair Representation is to reduce bias by decreasing the semantic distance between biassed terms. The goal of fair representation learning is to guarantee that representations are useful for a variety of prediction tasks and that sensitive aspects of the original data cannot be extracted from them. Adversarial training, which combines an encoder aiming to turn data into a fair representation with an adversary attempting to recover sensitive features from the representation, is the most used method for learning fair representations. However, recent research has discovered that these methods do not provide completely fair representations: stronger opponents can recover sensitive features. This could allow malicious or uneducated users to discriminate using the available representations. The issue of fair representation has lately risen in prominence as regulators draught guidelines on the ethical use of AI, indicating that any company that cannot ensure non-discrimination would be held liable for the data produced. Continue Reading Paper: https://openreview.net/pdf?id=BrFIKuxrZE Github: https://github.com/eth-sri/fnf submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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I am trying to use reinforcement for combinatorial optimization. I coded up a toy example for the travelling salesman but I am confused by something. In stable baselines 3 there is a variable "done". If I set it to optimum value (that is the length of the shortest route through all the cities computer by a different method) the RL algorithm never finds it. What should done be set to? Or more generally how do you do optimization of this sort using RL. I can just set it so that it is done after 1000 steps but then it is spending a lot of time finding the best way to take the first 1000 steps which isn't quite the point. submitted by /u/wiggyhat [link] [comments]  ( 2 min )

I was reading this paper and in section 3 they claim that agents act simultaneously and there is no notion of turn-taking: https://arxiv.org/abs/2104.07750 I was wondering how this works. What I'm used to seeing is a for loop in which, one after the other, agents execute the step function and interact with the environment. How does this change if all agents act simultaneously? submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

Hello everyone. I built a use case to instruct an agent, which simulates a herd of predators, to encircle a prey. Episodes are non-terminal and have a fixed number of steps. The agent uses a neural network to decide which actions to take at each step and is instructed by a remember/replay mechanism using a memory of past events. I started the experiment using absolute coordinates in the state returned by the environment and the result is displayed in the following image, where the total reward values at the end of each episode ​​are displayed. https://preview.redd.it/oscet38c0gw81.png?width=640&format=png&auto=webp&s=092fc92ee56c919bdc1851bb9f1ef9bdc585bfde In this case the results were encouraging. However, I wanted to create a more generic use case, using vectors that represent the position of the prey with respect to predators instead of absolute coordinates, in order to make the task more generic. Unfortunately, the best result I was able to obtain is the one shown in the image below where, apparently, I have a deterioration after a certain number of episodes. ​ https://preview.redd.it/yoiygwxe0gw81.png?width=640&format=png&auto=webp&s=06b7082cc0ebb80e3f5e5ce8307d75cd6451f79b It seems to me a case of catastrophic forgetting (I could be wrong) and I managed to mitigate it by increasing the memory, retaining the results of the older episodes, lowering the learning rate of the neural network and using a learning decay algorithm, but I have not succeeded to eliminate the phenomenon completely. Anyone have any advice? submitted by /u/kaeldric__ [link] [comments]  ( 2 min )

Hi all! I've been implementing some of the model-free algorithms in recent times. Comparing their performance to open-source libraries, however, learning takes severely longer in a computational sense. I wonder what tricks libraries such as stable-baselines3 use to increase frames per seconds and accelerate policy updates. So far, I vectorized the environment sampling & the bottleneck seems to be computing the updates for the agent. Thanks a lot! submitted by /u/Internal-Brush4929 [link] [comments]  ( 1 min )

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Reinforcement learning (RL) is a machine learning training strategy that rewards desirable behaviors while penalizing undesirable ones. A reinforcement learning agent can perceive and comprehend its surroundings, act, and learn through trial and error in general. Although RL agents can heuristically solve some problems, such as assisting a robot in navigating to a specific location in a given environment, there is no guarantee that they will be able to handle problems in settings they have not yet encountered. The capacity of these models to recognize the robot and any obstacles in its path, but not changes in its surrounding environment that occur independently of the agent, which we refer to as exogenous noise, is critical to their success. Existing RL algorithms are not powerful enoug…  ( 2 min )

www.usage.ai ​ Usage AI bundles 3-year no-upfront RIs on AWS with guaranteed buyback -- so users get all the savings of 3-year RIs with none of the commitment. I helped engineer the product. Here to answer any questions! submitted by /u/usage-team [link] [comments]

I recently published a new blog post, which goes over how meta-learned optimizers work and how to implement them in JAX. JAX's composable function transforms make implementing meta-learning algorithms very straightforward. If you're interested in JAX or meta learning give it a read! Blog post: https://teddykoker.com/2022/04/learning-to-learn-jax/ Code: https://github.com/teddykoker/learning-to-learn-jax Original Paper (NeurIPS '16): https://arxiv.org/abs/1606.04474 submitted by /u/tomkoker [link] [comments]  ( 1 min )

We’ve released a new open source project - https://github.com/deepfence/FlowMeter - to analyze and classify packet captures using ML techniques. FlowMeter is an experimental project; we’re using it to evaluate how effectively we can train an ML model to discriminate between different types of traffic flows, e.g. normal and anomalous. You can use sample data from various sources (see the README), or gather packet captures using PacketStreamer https://github.com/deepfence/PacketStreamer or other pcap tools. More information in the README, here: https://github.com/deepfence/FlowMeter and this blogpost: https://medium.com/@siddharthsatpathy.ss/introducing-flowmeter-97e0507862b6 Hope some people find it useful; we’d welcome any feedback, thank you. submitted by /u/sidd_ss [link] [comments]  ( 1 min )

https://gradio.app/ is for demoing machine learning models https://reddit.com/link/ueod3x/video/75e1ozmjihw81/player Prerequisite: Python 3.7+ and that's it! Quick Start To get Gradio running with a simple "Hello, World" example, follow these three steps: Install Gradio from pip. ​ pip install gradio Run the code below as a Python script or in a Python notebook (or in a colab notebook). ​ import gradio as gr def greet(name): return "Hello " + name + "!!" demo = gr.Interface(fn=greet, inputs="text", outputs="text") if __name__ == "__main__": demo.launch() The interface below will appear automatically within the Python notebook, or pop in a browser on http://localhost:7860 if running from a script. see more in the getting started guide: https://gradio.app/getting_started/ submitted by /u/Illustrious_Row_9971 [link] [comments]  ( 2 min )

Hi guys I thought you might find my project useful. It's called Reloadium and saves a lot of time during python development, especially in data science and machine learning field. More details here: https://github.com/reloadware/reloadium Hot Reloading dataframes Modifying and fixing code during debugging What do you guys think about it? submitted by /u/kwazar90 [link] [comments]  ( 1 min )

The SHAP method assumes linear relationship between the feature effects (see definition "Additive feature attribution methods" in the paper). But is this assumption realistic? submitted by /u/savoga [link] [comments]  ( 1 min )

Hi. I'm trying to come up with a solution for a medical image annotation system for our laboratory. We need it to be self-hosted (in order to only work in the uni's internal network), and open source (since the funds are limited). So far I've only found out about https://lab.vindr.ai/dashboard/projects but the documentation is really bad and I could not launch it using docker compose. I've also found MONAILabel(https://github.com/Project-MONAI/MONAILabel), but it apparently requires GPU which makes it really expensive. I'd rather find a cpu based solution because our task is not that complex. We only get some Dicom files (each have studies in them), and want to label them. submitted by /u/feryet [link] [comments]  ( 1 min )

https://medium.com/@subin.sen7/our-client-is-the-worlds-best-fpl-player-this-is-not-clickbait-51481fae76c9 Is it possible to use models to beat the market in Fantasy Football? submitted by /u/AI_FPL [link] [comments]

Hello everyone! Following this post numpy in fhe we are releasing a new lib that allows popular machine learning frameworks to run over encrypted data: https://github.com/zama-ai/concrete-ml Currently this supports xgboost and many sklearn models. We also support pytorch to some extent. We are trying to closely follow sklearn API (when relevant) to make the use easy to machine learning practitioners. Happy to hear any feedback on this ! submitted by /u/strojax [link] [comments]  ( 1 min )

Last time I used Tensorflow was 3 years ago and it was a resource hog as well as very slow. I intend to get back into machine learning and wondering if I should give Tensorflow.js a go again using the Nodejs backend since this will be an electron app. Or should I just go straight for Tensorflow. Ideally I will need about 15 fps while taking up minimal resources submitted by /u/manrayboy [link] [comments]  ( 1 min )

Hey r/MachineLearning, I'm Derrick from Layer (layer.ai) - the collaboration-first machine learning platform that enables you to build, train, track, and share your ML projects simply with a few lines of code. We are soft-launching today! I’ve been working on Layer for the past 2 years with an awesome team around the world. We really poured our hearts and minds into Layer and hope you will like it. Your feedback would be very appreciated! Layer Demo To get started, you can simply run our Quickstart Example! How is Layer different from other tools? Although there are plenty of ML and DS tooling products, we believe that there is still a large gap around collaboration. Many data science projects are hosted on GitHub, which, in our experience, does not provide sufficient depth and abs…  ( 4 min )

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A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.  ( 7 min )

Their model’s predictions should help researchers improve ocean climate simulations and hone the design of offshore structures.  ( 6 min )

Posted by Dhruv Shah, Intern, and Brian Ichter, Research Scientist, Robotics at Google Advances in reinforcement learning (RL) for robotics have enabled robotic agents to perform increasingly complex tasks in challenging environments. Recent results show that robots can learn to fold clothes, dexterously manipulate a rubik’s cube, sort objects by color, navigate complex environments and walk on difficult, uneven terrain. But "short-horizon" tasks such as these, which require very little long-term planning and provide immediate failure feedback, are relatively easy to train compared to many tasks that may confront a robot in a real-world setting. Unfortunately, scaling such short-horizon skills to the abstract, long horizons of real-world tasks is difficult. For example, how would one trai…  ( 7 min )

"The Megalodon was a large bivalve, measuring up to 2.5 meters in length. Its shell was covered in spines, and it had a large, powerful jaw for crushing prey." Although the megalodon is the most widely known as a giant prehistoric shark, I recently learned that Megalodon  ( 4 min )

AI Weirdness: the strange side of machine learning  ( 1 min )

It’s Monday morning, and Paul opens one of several emails sent by his boss, Heather. Her email seems a bit unusual, asking Paul to rush to… Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 4 min )

Deep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications. Notable examples include DeepMind’s work on controlling a nuclear reactor or on improving Youtube video compression, or Tesla attempting to use a method inspired by MuZero for autonomous vehicle behavior planning. But the exciting potential for real world applications of RL should also come with a healthy dose of caution - for example RL policies are well known to be vulnerable to exploitation, and methods for safe and robust policy development are an active area of research. At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine …  ( 8 min )

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Naive question: how do you pass a global observation of the environment to your actor and critic? In other words, is a global observation always available or does it depend on the environment? Can you give me a couple of examples? Thanks :) submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

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Parallel environments can make the learning much more efficient, but sometimes using parallel environments seemed affect the performance. Does anyone know why and how to solve? submitted by /u/Traditional-Ad4492 [link] [comments]  ( 1 min )

My current work requires me to work with GVRP. The specific problem is for an EV (agent) to find the optimal route to deliver items based on charge consumption and not discharging. I'm currently modelling it as a graph datastructure although it could be different. Are there any papers (both RL and exact method based) for state of the art that I can look into regarding this. I'm also interested in earlier papers that can help me build understanding, and I can increase complexity slowly. Thanks for the help! submitted by /u/evilBotman [link] [comments]  ( 1 min )

As above. I am interested in RL for robotics, specifically for legged locomotion. I wish to explore RL training on the real robot. Sample efficiency is paramount. Has any progress been made by utilizing, say, RNNs/LSTMs or even Attention ? submitted by /u/pakodanomics [link] [comments]  ( 1 min )

In my current environment, the agent is fed observations from a particular mathematical computation based upon the underlying state. This mathematical computation has hyper-parameters that influence the resulting observation given to the agent. Does it make any sense whatsoever to give these hyper-parameters to the agent within the action space? In this way the agent would be able to adjust its future observations and perhaps it would be able to use this in a smart way. On the other hand, I've heard that changing the environment from under the agents feed during training can lead to major issues with training. One can imagine that learning a dynamic environment is harder than a static one. Any thoughts? submitted by /u/C_BearHill [link] [comments]  ( 1 min )

Hello! If you are using jax and you need to pass some pytrees between processes, I may have something for you :) I developed a "treequeue". It is a queue that is made for pytree's nested arrays. The transfer speed is up to 10 times higher than regular queues. This is done by utilizing shared memory arrays and avoiding pickling data. This can be very useful when developing distributed architecture, e.g. distributed reinforcement learning where speed is at the upmost importance. In my case this implementation was very useful to remove bottlenecks when implementing RL PBT algorithms! https://github.com/thomashirtz/treequeues Cheers! submitted by /u/krenast [link] [comments]  ( 1 min )

Hi, I have problems the agent forgets good states during self-supervised pre-training. It shows good exploration, but as time goes by, only the edge case is explored, showing poor performance at finetune. I found a paper about that before, but It is really hard to find again. It related to reward. Could you recommend a paper related to this? Thanks for reading. submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 1 min )

For personal context: I worked over time during my bachelors and took grad classes/started research, but financial situation took a hit for the worse and we'll let's say I don't have enough money for a Masters/continue my studies. I'm job hunting now but in a chicken/egg problem most of these jobs want at the very least research (which I know how to conduct) for which they have the resources for. Either way I have a couple research topics I want to explore, but limited resources and want to be realistic here. main question: do you know of anyone who has done it as a non-grad/non-institutional way without the help of established figures in the field. I only know of those that have done it in the way I describe above (either under an established researcher's team, a university, or a company)? Add-on: would appreciate any personal experiences as well, and if anyone has experience with conferences/how long it takes, etc. My research experience has been largely under NDAs so I haven't experienced formal publishing yet (but want to!) submitted by /u/robml [link] [comments]  ( 2 min )

Is there any rule of thumb to decide from which layer extract feature embeddings for classification tasks based on knn? Does it depend by the architecture ?(conv net , vs vit models, ecc) Should I extract before or after activation function? Edit: Which model do you suggest to start from ? Actually i'm using a resnet50 trained with DINO submitted by /u/Rich_Freedom98 [link] [comments]  ( 1 min )

Terraform Provider Iterative (TPI) address the specific needs of machine learning teams - it is an open-source tool extending the functionality of Terraform, the world's most widely used multi-cloud provisioning product. The tool enables full lifecycle management of computing resources and is designed specifically for machine learning pipelines: Terraform plugin for machine learning workloads: spot instance recovery & auto-termination | AWS, GCP, Azure, Kubernetes The tool aims to bridge the gap between devops and data science teams and build on top of Terraform, a tool universally familiar to devops teams, but extend it to suit machine learning needs. It provides to following advantages for your ML workflow: Lower cost: use your preferred cloud provider's existing pricing, including on-demand per-second billing and bulk discounts. Auto-recovery: spot/preemptible instances are cheap but unreliable. TPI reliably and automatically respawns such interrupted instances, caching & restoring the working directory in the cloud even when you are offline. Custom spec: full control over hardware & software requirements via a single config file. submitted by /u/cmstrump [link] [comments]  ( 1 min )

Paper (pdf). A link to the paper is also in this blog post. Abstract: Building models that can be rapidly adapted to numerous tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. Flamingo models include key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We perform a thorough evaluation of the proposed Flamingo models, exploring and measuring their ability to rapidly adapt to a variety of image and video understanding benchmarks. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer, captioning tasks, which evaluate the ability to describe a scene or an event, and close-ended tasks such as multiple choice visual question-answering. For tasks lying anywhere on this spectrum, we demonstrate that a single Flamingo model can achieve a new state of the art for few-shot learning, simply by prompting the model with task-specific examples. On many of these benchmarks, Flamingo actually surpasses the performance of models that are fine-tuned on thousands of times more task-specific data. submitted by /u/Wiskkey [link] [comments]  ( 1 min )

Hello all, I am doing my PhD on numerical modelling of solar radiation and I am researching SOMs as a possible tool. I have this problem where I can't decide on a network size, so I have been trying to develop some means to compare SOM neurons from different sized networks. I have read that the 2-d SOM array tends to spam the 2 highest variance principal components. So would it be adecuate to tag my SOM nodes with the first two proyections of PCA? So that I can compare results from different experiments. submitted by /u/juliancanellas [link] [comments]  ( 1 min )

Hello guys, I'm wondering if you can help: we use machine learning models to identify trading opportunities using historical trading data of price and market indicators. The data we use is fine: abundant, accurate and (manually) proven to work over several non-sequential months in the past. The problem is that we keep on getting the exact same MAE result. Any suggestions on solving this issue would be immensely appreciated. submitted by /u/AFAC1410 [link] [comments]  ( 1 min )

Hi folks, our blog post on how to automatically find label errors in audio datasets has just gone live. We cover the steps to: ⛏️ Perform feature extraction (aka embeddings) on the Spoken Digit dataset with a pre-trained PyTorch model. 🔢 Use cross-validation to generate out-of-sample predicted probabilities for every example in the dataset. 🏷️ Run one line of cleanlab code on these predicted probabilities to identify which audio clips may be mislabeled. 📰 Blog Post + Google Colab: https://cleanlab.ai/blog/label-errors-audio-datasets/ https://preview.redd.it/vpzhwg6jebw81.png?width=1260&format=png&auto=webp&s=3fa79d8a097ae5936e5e6a51e87508adf6190835 submitted by /u/weijinglok [link] [comments]  ( 1 min )

https://arxiv.org/abs/2202.03265?context=eess Computer vision approach to processing naturalistic brain responses to music. Models achieved state of the art and they used publicly available datasets. With 1 sec of brain signal they can classify the name of the song you're listening to and how much you enjoyed it ~89%. Imagine this but on your airpods, seems wild. submitted by /u/blackliquerish [link] [comments]

It’s looking more and more like I won’t be able to get any top tier publications in my PhD (NeurIPS, ICLR, etc.). I’ve tried, but various factors have limited the experience. I’m curious what people were able to do in this circumstance. I’ll have 3-5 publications in mid-tier venues, plus many years of experience as a software engineer and machine learning engineer (in non-tech companies). People in my program have gone on to work at Amazon, and a few Google, but these were all applied scientist and engineering roles. Does anyone go on to work as a research scientist? National labs seem like a good bet, I’m curious what others have done. submitted by /u/walterkronkite33 [link] [comments]  ( 5 min )

It seems to me that the ML research community has stopped talking about agents? As in, machines that act in complex environments by themselves? Ever since GPT-3 came out, bascially every single paper has been related to NLP or Transformers in general. It seems a natural next step would be to try to get agents we could tell what to do and how to do it in natural language, no? Yet that has not been the focus at all. The last time we had wide spread focus on planning and acting was the Starcraft challenge, but that was "solved" so quickly nothing much came of it. Research I know about: - Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents - Tesla in general I guess, with their cars and maybe robots Is it just me or is there just no interest in researching more capable agents right now? submitted by /u/ReasonablyBadass [link] [comments]  ( 2 min )

There are a bunch of papers on different advertisement allocation techniques (placing ads to valid ad spots) and in algorithms in grad school I learned MaxFlow/MinCut, but I'm wondering what is actually being used in production systems? Linear models, genetic algorithms, reinforcement learning, some neural network? I know that very little of this is published/talked about due to being proprietary. However, I'm wondering because so much gets published, but the more advanced algorithms tend to be complex and not necessarily possible to be deployed in production systems (what I suspect)? submitted by /u/Flipper3 [link] [comments]  ( 2 min )

Hi Folks! It'd really help if you can participate in this poll and share about your ML/DL model workflow before moving it to production. Out of these steps, which one is your most preferred way of integrating ML into your app or business (not specific to any domain): View Poll submitted by /u/fgp121 [link] [comments]  ( 1 min )

My current work requires me to work with GVRP. The specific problem is for an EV (agent) to find the optimal route to deliver items based on charge consumption and not discharging. I'm currently modelling it as a graph datastructure although it could be different. Are there any papers (both RL and exact method based) for state of the art that I can look into regarding this. I'm also interested in earlier papers that can help me build understanding, and I can increase complexity slowly. Thanks for the help! submitted by /u/evilBotman [link] [comments]  ( 1 min )

I have read several blogs in which they specified that you should clip your gradients to the largest value that doesn't cause exploding gradients. But that also means that you could get a situation that you don't need to do gradient clipping at all. Could that hurt convergence speed in some way? Is there any guideline for how to pick this hyperparameter based on learning rate, batch size, model size, input size (seq len, img size) etc.? Or is this project/use case dependent and it represents "just another hyperparameter"? submitted by /u/Icy_Fisherman7187 [link] [comments]  ( 1 min )

I'd like to show off a Hungarian NLP pipeline which we've been heavily improving over the past year. https://github.com/huspacy/huspacy While processing Hungarian texts might not be interesting for the most of you, I believe this project can be a good learning resource, as our models utilize the latest NLP technologies from Explosion and are fully reproducible: We've created a transformers-based model on top of a language specific BERT model Multi-task and transfer-learning is heavily used across the models. Incorporated edit-tree lemmatization and biaffine parsing from spacy-experimental We provide word embeddings using the (fastText-like) floret tool submitted by /u/oroszgy [link] [comments]  ( 1 min )

I'm trying to tune the parameters of two models - a random forest classifier, and a gradient boosting classifier. When using a grid search or a random search (I might also play around with the genetic algorithm), what are appropriate ranges to use / how can I come to know them? I obviously can't just specify a very arbitrary range because that might decrease the effectiveness of the random search, and make the grid search too long. Do they depend on the number of features in the dataset or other characteristics (e.g. our dataset is quite imbalanced and we're using SMOTE resampling)? I'm just trying to tune n_estimators and max_depth, but RandomForestClassifier also has many other parameters, do I just experiment with all of them, or are there any known parameters that don't do anything useful unless I'm trying for an extra 0.1% of accuracy or recall? Basically the same questions as above for the GradientBoostingClassifier. Letting go of specific models for a second, I'd appreciate generic pointers on how to deliberately tune hyperparameters or specify ranges instead of just arbitrarily specifying sth and hoping it works. Thanks! submitted by /u/stuffingmybrain [link] [comments]  ( 2 min )

With big players like OpenAI and Google building these massive models, how does independent researchers without access to such scale and compute do meaningful work? Came across tweets from researchers, especially ones working on generative models saying they feel their work looks irrelevant after seeing results from DALL-E 2. It feels like just a couple of years ago if you had a decent GPU setup, you could pretty much do world class research. Doesn't look like it anymore. Is there, if any, research directions that makes it a level playing field where compute and scale is not necessarily the solution, or are we all doomed to be prompt engineers for GPT models? submitted by /u/HairyIndianDude [link] [comments]  ( 4 min )

Dall-e 2 has been released a few weaks and is exceptionnally capable for generating images from a text description. However training data containing porn has been filtered out as well as requests that contains nsfw terms. So is it not suited for generated porn for now. However, how long would it takes before : Someone does something as capable and allows porn OpenAI opens dall-e 2 to such content (edit)This is an open discussion.But there is clear evidence that the technology now exists to create on demand and instantly any kind of porn with any kind of kink in a lot of styles including photorealistic style. submitted by /u/exotic_deviantcy [link] [comments]

We often believe that future soldiers are going to be robots. There will no longer be human "boots on the ground" thanks to artificial intelligence, drones, etc. Many of us fail to realize that current soldiers, in militaries across the globe, are being augmented with AI as we speak. Things like AR headsets, computerized sights, and other technologies are going to turn them into cyborgs...Another one of the intriguing aspects of this is that major companies like Microsoft are creating these technologies for the military. How long until we see augmented soldiers going against one another? https://www.linkedin.com/pulse/augmented-soldier-mvyl-associates-1f/?trackingId=FnvaWMtxMsfm2fJeQP1bgg%3D%3D submitted by /u/IsabeldeMontoya [link] [comments]  ( 1 min )

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I am the instructor and lead of the AI program at a community college in North Carolina. I am developing the program currently and have money to spend on Cool Educational Robotics and potentially other tools. Does anybody have any recommendations for robots to teach python with classic search, reinforcement learning methods, or other university level algorithms? A robotic arm would be nice, but I need to tie it directly to a potential project in machine learning, deep learning, reinforcement learning, search, logic, computer vision, etc. We have a NAO robot and are getting a LIMO: https://www.robotlab.com/store/limo-agilex But we could probably use an Educational Robotic Arm Thanks! Our program is one of the first Associates programs in AI in the country. Check out our program website. It is completely available online and we accept out of state students. https://www.waynecc.edu/programs/ai/ Feel free to ask any questions as well. *Also, advice on best value cloud computing virtualized resources for high performance computing for deep learning projects is appreciated. We are currently planning on going with Microsoft's Data Science Virtual Machines but not sure which series exactly submitted by /u/Wayne_CC_AI [link] [comments]  ( 1 min )

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Hello everyone, I wanted to know if one could get access to the dall-e 2 app without having to hope to be chosen from the wait list. Thanks! submitted by /u/Swaggyswaggerson [link] [comments]  ( 1 min )

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Our Latest research report on the Artificial Intelligence of Things (AIoT) market shows how that market is changing and how trends in demographics, business cycles, and microeconomics affect the Artificial Intelligence of Things (AIoT) market as a whole. Our study of the global Artificial Intelligence of Things (AIoT) market demonstrates what's happening with business state by looking at production value and key regions. The market report provides an entire analysis of sales volume, pricing analysis, revenue, the margin of profit, the expansion rate within the Artificial Intelligence of Things (AIoT) market. Get A Free Sample Report @ https://www.intelligencemarketreport.com/report-sample/573982 ​ \"𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐨𝐟 𝐓𝐡𝐢𝐧𝐠𝐬 \" Key Information Extracted from the Report ​ Extensive information on factors estimated to affect the Market growth and market share during the forecast period is presented in the report. The report offers the present scenario and future growth prospects Market in various geographical regions. The competitive landscape analysis on the market as well as the qualitative and quantitative information is delivered. The SWOT analysis is conducted along with Porter's Five Force analysis. · The in-depth analysis provides an insight into the Market, underlining the growth rate and opportunities offered in the business. Leading Key Players Included In This Report Are: · Twilio Inc. · ShiftPixy Inc. · Micron Technology · Intel · IBM · Gopher Protocol · Deep Vision · Ceva · ALCES · AISPEECH submitted by /u/Purva_Duggal [link] [comments]

Automated CV Pipelines 4th part is open for registration. It will be covering some of the best practices for video-specific annotation tasks. If you are interested you can check out the details here! submitted by /u/WeekendClassic [link] [comments]

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Hey guys! I’m excited to share with you an exciting upcoming hackathon, High Tech Hacks 2.0! High Tech Hacks is a free, international 24-hour hackathon on May 21-22nd, 2022 open to all high schoolers hoping to learn a new coding skill, compete for awesome prizes, or work with other like-minded hackers. Let’s invent, create, and push the boundaries of technology (as much as we can at one hackathon)! What to expect: Last year, participants learned the basics of web development, Python, virtual reality, and how to make a Discord bot from current software engineers at Microsoft, Amazon, Twilio, other tech companies, and Columbia University SHPE. Thanks to our company sponsors, each participant last year received nearly $400 worth of free software and swag. Register to earn FREE swag (t-shirts, water bottles, stickers!) Network with other passionate STEM high school students from around the world! (Last year we had participants from 26 countries signed up already!) This year we have even bigger prizes, competitions, and speakers so stay tuned! Reach out to me with more questions or email [hightechhackathon@gmail.com](mailto:hightechhackathon@gmail.com). Happy hacking! :D Sign up here to confirm your interest and get on our mailing list: Click Here to Register! Also, meet other hackers by Joining our Discord! For more, Check out our Website submitted by /u/HighTechHacks [link] [comments]  ( 1 min )

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Today, AWS announced the general availability of Amazon Rekognition Streaming Video Events, a fully managed service for camera manufacturers and service providers that uses machine learning (ML) to detect objects such as people, pets, and packages in live video streams from connected cameras. Amazon Rekognition Streaming Video Events sends them a notification as soon as […]  ( 8 min )

3xLOGIC is a leader in commercial electronic security systems. They provide commercial security systems and managed video monitoring for businesses, hospitals, schools, and government agencies. Managed video monitoring is a critical component of a comprehensive security strategy for 3xLOGIC’s customers. With more than 50,000 active cameras in the field, video monitoring teams face a daily […]  ( 4 min )

Abode Systems (Abode) offers homeowners a comprehensive suite of do-it-yourself home security solutions that can be set up in minutes and enables homeowners to keep their family and property safe. Since the company’s launch in 2015, in-camera motion detection sensors have played an essential part in Abode’s solution, enabling customers to receive notifications and monitor […]  ( 6 min )

Amazon SageMaker Data Wrangler reduces the time to aggregate and prepare data for machine learning (ML) from weeks to minutes. With Data Wrangler, you can select and query data with just a few clicks, quickly transform data with over 300 built-in data transformations, and understand your data with built-in visualizations without writing any code. Additionally, […]  ( 4 min )

Searchmetrics is a global provider of search data, software, and consulting solutions, helping customers turn search data into unique business insights. To date, Searchmetrics has helped more than 1,000 companies such as McKinsey & Company, Lowe’s, and AXA find an advantage in the hyper-competitive search landscape. In 2021, Searchmetrics turned to AWS to help with […]  ( 5 min )

Identifying paraphrased text has business value in many use cases. For example, by identifying sentence paraphrases, a text summarization system could remove redundant information. Another application is to identify plagiarized documents. In this post, we fine-tune a Hugging Face transformer on Amazon SageMaker to identify paraphrased sentence pairs in a few steps. A truly robust […]  ( 10 min )

This is a guest post by Moovit’s Software and Cloud Architect, Sharon Dahan. Moovit, an Intel company, is a leading Mobility as a Service (MaaS) solutions provider and creator of the top urban mobility app. Moovit serves over 1.3 billion riders in 3,500 cities around the world. We help people everywhere get to their destination […]  ( 7 min )

Featuring stunning visuals from futuristic interstellar worlds, including colossal sand creatures, Dune captivated audiences around the world. The sci-fi film picked up six Oscars last month at the 94th Academy Awards, including for Best Sound and Visual Effects. Adapted from Frank Herbert’s 1965 novel of the same name, Dune tells the story of Paul Atreides, Read article > The post How DNEG Helped Win Another Visual-Effects Oscar by Bringing ‘Dune’ to Life With NVIDIA RTX appeared first on NVIDIA Blog.  ( 4 min )

It’s a jam-packed GFN Thursday. This week brings the popular, free-to-play, action role-playing game Lost Ark to gamers across nearly all their devices, streaming on GeForce NOW. And that’s not all. GFN Thursday also delivers an upgraded experience in the 2.0.40 update. M1-based MacBooks, iMacs and Mac Minis are now supported natively. Plus, membership gift Read article > The post Your Odyssey Awaits: Stream ‘Lost Ark’ to Nearly Any Device This GFN Thursday appeared first on NVIDIA Blog.  ( 4 min )

The 2019 pandemic became a catalyst for retail businesses and their customers. Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 6 min )

Workshop hosted by MIT’s Climate and Sustainability Consortium, MIT-IBM Watson AI Lab, and the MIT Schwarzman College of Computing highlights how new approaches to computing can save energy and help the planet.  ( 8 min )

Competitive seed grants launch yearlong investigations of novel hypotheses about potential causes, biomarkers, treatments of Alzheimer’s and ALS.  ( 6 min )

Hello, I really need help creating a simple Neural Network that approximates the Rosenbrock function (a function with two variables). I have used the book "MATLAB Deep Learning" by Phil Kim, which provides code examples. However, when I make a simple example where the network is trained with backpropagation the output of the testing points simply comes out as ones. It is very early work so the code is very simple. I have just cut out the piece of code with Neural Network. Can someone give any tips or help me to figure out what is wrong? %% Neural Network % Rosenbrock function : f = (1 - x1).^2 + 100*(x2-x1.^2).^2 % X: 25x2 matrix % f_samp: 25x1 vector % Number of nodes in hidden layer nHNodes = 4 ; % Input layer has two nodes % Output layer has one node % Initial weigts W1 = 2*rand(nHNodes, k) - 1 ; W2 = 2*rand(1, nHNodes) - 1 ; alpha = 0.5 ; % Lerning rate % Backpropagation for i = 1:10000 for i = 1:size(X,1) x = X(i, :)'; d = f_samp(i); % Forward v1 = W1*x; y1 = fun_sigmoid(v1); v = W2*y1; y = fun_sigmoid(v); % Backward e = d - y; delta = y.*(1-y).*e; e1 = W2'*delta; delta1 = y1.*(1-y1).*e1; dW1 = alpha*delta1*x'; W1 = W1 + dW1; dW2 = alpha*delta*y1'; W2 = W2 + dW2; end end % Testing sampling points for i = 1:size(X,1) x = X(i, :)' ; v1 = W1*x ; y1 = fun_sigmoid(v1) ; v = W2*y1 ; y(i) = fun_sigmoid(v) ; end submitted by /u/TobiasFred [link] [comments]  ( 1 min )

Here's my code: https://github.com/heyuhowudoin/mnist_ai I currently have 3 layers, 15, 15 and 10 neurons respectively. I'm using the MNIST database to get images of hand-drawn numbers and trying to classify them by which neuron in the final layer has the highest activation. I use a combination of sigmoid and ReLU as activation functions, and I am using minibatches of 100 pictures each. The thing I see happening is that it converges to a point where every neuron in the final layer outputs a 0 because then it has a relatively low error since the target for 9 out of 10 of those neurons is indeed 0, whereas the target for the correct output neuron is 1. I've tried looking over my backprop algorithm, I've tried changing the neurons in each hidden layer to 200, I've changed the learning speed a bunch but I can't seem to figure out why it's not working so if one of you guys wouldn't mind helping me, that would be much appreciated. submitted by /u/-i-hate-this-place- [link] [comments]  ( 1 min )

Hello, I have an alphago zero question. Why doesn’t alphago zero use Q(s,a) to choose its next move in the Monte Carlo tree search? Why does it use the π instead? submitted by /u/Skinnybisquit [link] [comments]

Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier-Stokes and finite elasticity is computationally expensive. Optimizing robotic swimmer design within such a system generally involves cumbersome, gradient-free procedures on top of the already costly simulation. To address this challenge we present a novel, fully differentiable hybrid approach to FSI that combines a 2D direct numerical simulation for the deformable solid structure of the swimmer and a physics-constrained neural network surrogate to capture hydrodynamic effects of the fluid. For the deformable simulation of the swimmer's body, we use state-of-the-art techniques from the field of computer graphics to speed up the finite-element method (FEM). For the fluid simulation, we use a U-Net architecture trained with a physics-based loss function to predict the flow field at each time step. The pressure and velocity field outputs from the neural network are sampled around the boundary of our swimmer using an immersed boundary method (IBM) to compute its swimming motion accurately and efficiently. We demonstrate the computational efficiency and differentiability of our hybrid simulator on a 2D carangiform swimmer. Since both the solid simulator and the hydrodynamics model are automatically differentiable, we obtain a fully differentiable FSI simulator that can be used for computational co-design of geometry and controls for rigid and soft bodies immersed in fluids, such as minimizing drag, maximizing speed, or maximizing efficiency via direct gradient-based optimization.  ( 2 min )

Industry 4.0-enabled smart factory is expected to realize the next revolution for manufacturers. Although artificial intelligence (AI) technologies have improved productivity, current use cases belong to small-scale and single-task operations. To unbound the potential of smart factory, this paper develops zero-touch network systems for intelligent manufacturing and facilitates distributed AI applications in both training and inferring stages in a large-scale manner. The open radio access network (O-RAN) architecture is first introduced for the zero-touch platform to enable globally controlling communications and computation infrastructure capability in the field. The designed serverless framework allows intelligent and efficient learning assignments and resource allocations. Hence, requested learning tasks can be assigned to appropriate robots, and the underlying infrastructure can be used to support the learning tasks without expert knowledge. Moreover, due to the proposed network system's flexibility, powerful AI-enabled networking algorithms can be utilized to ensure service-level agreements and superior performances for factory workloads. Finally, three open research directions of backward compatibility, end-to-end enhancements, and cybersecurity are discussed for zero-touch smart factory.  ( 2 min )

A common technique in high energy physics is to characterize the response of a detector by means of models tunned to data which build parametric maps from the physical parameters of the system to the expected signal of the detector. When the underlying model is unknown it is difficult to apply this method, and often, simplifying assumptions are made introducing modeling errors. In this article, using a waveform toy model we present how deep learning in the form of constrained bottleneck autoencoders can be used to learn the underlying unknown detector response model directly from data. The results show that excellent performance results can be achieved even when the signals are significantly affected by random noise. The trained algorithm can be used simultaneously to perform estimations on the physical parameters of the model, simulate the detector response with high fidelity and to denoise detector signals.  ( 2 min )

Research in cognitive science has provided extensive evidence of human cognitive ability in performing physical reasoning of objects from noisy perceptual inputs. Such a cognitive ability is commonly known as intuitive physics. With advancements in deep learning, there is an increasing interest in building intelligent systems that are capable of performing physical reasoning from a given scene for the purpose of building better AI systems. As a result, many contemporary approaches in modelling intuitive physics for machine cognition have been inspired by literature from cognitive science. Despite the wide range of work in physical reasoning for machine cognition, there is a scarcity of reviews that organize and group these deep learning approaches. Especially at the intersection of intuitive physics and artificial intelligence, there is a need to make sense of the diverse range of ideas and approaches. Therefore, this paper presents a comprehensive survey of recent advances and techniques in intuitive physics-inspired deep learning approaches for physical reasoning. The survey will first categorize existing deep learning approaches into three facets of physical reasoning before organizing them into three general technical approaches and propose six categorical tasks of the field. Finally, we highlight the challenges of the current field and present some future research directions.  ( 2 min )

Continuous evolution in modern software often causes documentation, tutorials, and examples to be out of sync with changing interfaces and frameworks. Relying on outdated documentation and examples can lead programs to fail or be less efficient or even less secure. In response, programmers need to regularly turn to other resources on the web such as StackOverflow for examples to guide them in writing software. We recognize that this inconvenient, error-prone, and expensive process can be improved by using machine learning applied to software usage data. In this paper, we present our practical system which uses machine learning on large-scale telemetry data and documentation corpora, generating appropriate and complex examples that can be used to improve documentation. We discuss both feature-based and transformer-based machine learning approaches and demonstrate that our system achieves 100% coverage for the used functionalities in the product, providing up-to-date examples upon every release and reduces the numbers of PRs submitted by software owners writing and editing documentation by >68%. We also share valuable lessons learnt during the 3 years that our production quality system has been deployed for Azure Cloud Command Line Interface (Azure CLI).  ( 2 min )

Classical accuracy-oriented Recommender Systems (RSs) typically face the cold-start problem and the filter-bubble problem when users suffer the familiar, repeated, and even predictable recommendations, making them boring and unsatisfied. To address the above issues, serendipity-oriented RSs are proposed to recommend appealing and valuable items significantly deviating from users' historical interactions and thus satisfying them by introducing unexplored but relevant candidate items to them. In this paper, we devise a novel serendipity-oriented recommender system (\textbf{G}enerative \textbf{S}elf-\textbf{S}erendipity \textbf{R}ecommender \textbf{S}ystem, \textbf{GS$^2$-RS}) that generates users' self-serendipity preferences to enhance the recommendation performance. Specifically, this model extracts users' interest and satisfaction preferences, generates virtual but convincible neighbors' preferences from themselves, and achieves their self-serendipity preference. Then these preferences are injected into the rating matrix as additional information for RS models. Note that GS$^2$-RS can not only tackle the cold-start problem but also provides diverse but relevant recommendations to relieve the filter-bubble problem. Extensive experiments on benchmark datasets illustrate that the proposed GS$^2$-RS model can significantly outperform the state-of-the-art baseline approaches in serendipity measures with a stable accuracy performance.  ( 2 min )

Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We first unveil the mechanisms responsible for this shortcoming, demonstrating how models rely on shortcuts that leverage the topology of the protein-ligand bipartite network, rather than learning the node features. Then, we introduce AI-Bind, a pipeline that combines network-based sampling strategies with unsupervised pre-training, allowing us to limit the annotation imbalance and improve binding predictions for novel proteins and ligands. We illustrate the value of AI-Bind by predicting drugs and natural compounds with binding affinity to SARS-CoV-2 viral proteins and the associated human proteins. We also validate these predictions via auto-docking simulations and comparison with recent experimental evidence, and step up the process of interpreting machine learning prediction of protein-ligand binding by identifying potential active binding sites on the amino acid sequence. Overall, AI-Bind offers a powerful high-throughput approach to identify drug-target combinations, with the potential of becoming a powerful tool in drug discovery.  ( 2 min )

Existing multi-view classification algorithms focus on promoting accuracy by exploiting different views, typically integrating them into common representations for follow-up tasks. Although effective, it is also crucial to ensure the reliability of both the multi-view integration and the final decision, especially for noisy, corrupted and out-of-distribution data. Dynamically assessing the trustworthiness of each view for different samples could provide reliable integration. This can be achieved through uncertainty estimation. With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level. The proposed TMC can promote classification reliability by considering evidence from each view. Specifically, we introduce the variational Dirichlet to characterize the distribution of the class probabilities, parameterized with evidence from different views and integrated with the Dempster-Shafer theory. The unified learning framework induces accurate uncertainty and accordingly endows the model with both reliability and robustness against possible noise or corruption. Both theoretical and experimental results validate the effectiveness of the proposed model in accuracy, robustness and trustworthiness.  ( 2 min )

A particular direction of recent advance about stochastic deep-learning algorithms has been about uncovering a rather mysterious heavy-tailed nature of the stationary distribution of these algorithms, even when the data distribution is not so. Moreover, the heavy-tail index is known to show interesting dependence on the input dimension of the net, the mini-batch size and the step size of the algorithm. In this short note, we undertake an experimental study of this index for S.G.D. while training a $\relu$ gate (in the realizable and in the binary classification setup) and for a variant of S.G.D. that was proven in Karmakar and Mukherjee (2022) for ReLU realizable data. From our experiments we conjecture that these two algorithms have similar heavy-tail behaviour on any data where the latter can be proven to converge. Secondly, we demonstrate that the heavy-tail index of the late time iterates in this model scenario has strikingly different properties than either what has been proven for linear hypothesis classes or what has been previously demonstrated for large nets.  ( 2 min )

Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pair it with the new evolution of Zion platform, namely ZionEX. We demonstrate the capability to train very large DLRMs with up to 12 Trillion parameters and show that we can attain 40X speedup in terms of time to solution over previous systems. We achieve this by (i) designing the ZionEX platform with dedicated scale-out network, provisioned with high bandwidth, optimal topology and efficient transport (ii) implementing an optimized PyTorch-based training stack supporting both model and data parallelism (iii) developing sharding algorithms capable of hierarchical partitioning of the embedding tables along row, column dimensions and load balancing them across multiple workers; (iv) adding high-performance core operators while retaining flexibility to support optimizers with fully deterministic updates (v) leveraging reduced precision communications, multi-level memory hierarchy (HBM+DDR+SSD) and pipelining. Furthermore, we develop and briefly comment on distributed data ingestion and other supporting services that are required for the robust and efficient end-to-end training in production environments.  ( 3 min )

Class imbalance distribution widely exists in real-world engineering. However, the mainstream optimization algorithms that seek to minimize error will trap the deep learning model in sub-optimums when facing extreme class imbalance. It seriously harms the classification precision, especially on the minor classes. The essential reason is that the gradients of the classifier weights are imbalanced among the components from different classes. In this paper, we propose Attraction-Repulsion-Balanced Loss (ARB-Loss) to balance the different components of the gradients. We perform experiments on the large-scale classification and segmentation datasets and our ARB-Loss can achieve state-of-the-art performance via only one-stage training instead of 2-stage learning like nowadays SOTA works.  ( 2 min )

Cardiovascular diseases (CVDs) have become the top one cause of death; three-quarters of these deaths occur in lower-income communities. Electrocardiography (ECG), an electrical measurement capturing the cardiac activities, is a gold-standard to diagnose CVDs. However, ECG is infeasible for continuous cardiac monitoring due to its requirement for user participation. Meanwhile, photoplethysmography (PPG) is easy to collect, but the limited accuracy constrains its clinical usage. In this research, a novel Transformer-based architecture, Performer, is invented to reconstruct ECG from PPG and to create a novel digital biomarker, PPG along with its reconstructed ECG, as multiple modalities for CVD detection. This architecture, for the first time, performs Transformer sequence to sequence translation on biomedical waveforms, while also utilizing the advantages of the easily accessible PPG and the well-studied base of ECG. Shifted Patch-based Attention (SPA) is created to maximize the signal features by fetching the various sequence lengths as hierarchical stages into the training while also capturing cross-patch connections through the shifted patch mechanism. This architecture generates a state-of-the-art performance of 0.29 RMSE for reconstructing ECG from PPG, achieving an average of 95.9% diagnosis for CVDs on the MIMIC III dataset and 75.9% for diabetes on the PPG-BP dataset. Performer, along with its novel digital biomarker, offers a low-cost and non-invasive solution for continuous cardiac monitoring, only requiring the easily extractable PPG data to reconstruct the not-as-accessible ECG data. As a prove of concept, an earring wearable, named PEARL (prototype), is designed to scale up the point-of-care (POC) healthcare system.  ( 2 min )

Nowadays, the interpretation of why a machine learning (ML) model makes certain inferences is as crucial as the accuracy of such inferences. Some ML models like the decision tree possess inherent interpretability that can be directly comprehended by humans. Others like artificial neural networks (ANN), however, rely on external methods to uncover the deduction mechanism. SHapley Additive exPlanations (SHAP) is one of such external methods, which requires a background dataset when interpreting ANNs. Generally, a background dataset consists of instances randomly sampled from the training dataset. However, the sampling size and its effect on SHAP remain to be unexplored. In our empirical study on the MIMIC-III dataset, we show that the two core explanations - SHAP values and variable rankings fluctuate when using different background datasets acquired from random sampling, indicating that users cannot unquestioningly trust the one-shot interpretation from SHAP. Luckily, such fluctuation decreases with the increase of the background dataset size. Also, we notice an U-shape in the stability assessment of SHAP variable rankings, demonstrating that SHAP is more reliable in ranking the most and least important variables compared to moderately important ones. Overall, our results suggest that users should take into account how background data affects SHAP results, with improved SHAP stability as the background sample size increases.  ( 2 min )

Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which training examples are 'guilty' in making the trained ML model predictions inaccurate or unfair?" Modeling data influence for ML training has attracted intensive interest over the last decade, and one popular framework is to compute the Shapley value of each training example with respect to utilities such as validation accuracy and fairness of the trained ML model. Unfortunately, despite recent intensive interest and research, existing methods only consider a single ML model "in isolation" and do not consider an end-to-end ML pipeline that consists of data transformations, feature extractors, and ML training. We present DataScope (ease.ml/datascope), the first system that efficiently computes Shapley values of training examples over an end-to-end ML pipeline, and illustrate its applications in data debugging for ML training. To this end, we first develop a novel algorithmic framework that computes Shapley value over a specific family of ML pipelines that we call canonical pipelines: a positive relational algebra query followed by a K-nearest-neighbor (KNN) classifier. We show that, for many subfamilies of canonical pipelines, computing Shapley value is in PTIME, contrasting the exponential complexity of computing Shapley value in general. We then put this to practice -- given an sklearn pipeline, we approximate it with a canonical pipeline to use as a proxy. We conduct extensive experiments illustrating different use cases and utilities. Our results show that DataScope is up to four orders of magnitude faster over state-of-the-art Monte Carlo-based methods, while being comparably, and often even more, effective in data debugging.  ( 2 min )

Explainability is crucial for probing graph neural networks (GNNs), answering questions like "Why the GNN model makes a certain prediction?". Feature attribution is a prevalent technique of highlighting the explanatory subgraph in the input graph, which plausibly leads the GNN model to make its prediction. Various attribution methods exploit gradient-like or attention scores as the attributions of edges, then select the salient edges with top attribution scores as the explanation. However, most of these works make an untenable assumption - the selected edges are linearly independent - thus leaving the dependencies among edges largely unexplored, especially their coalition effect. We demonstrate unambiguous drawbacks of this assumption - making the explanatory subgraph unfaithful and verbose. To address this challenge, we propose a reinforcement learning agent, Reinforced Causal Explainer (RC-Explainer). It frames the explanation task as a sequential decision process - an explanatory subgraph is successively constructed by adding a salient edge to connect the previously selected subgraph. Technically, its policy network predicts the action of edge addition, and gets a reward that quantifies the action's causal effect on the prediction. Such reward accounts for the dependency of the newly-added edge and the previously-added edges, thus reflecting whether they collaborate together and form a coalition to pursue better explanations. As such, RC-Explainer is able to generate faithful and concise explanations, and has a better generalization power to unseen graphs. When explaining different GNNs on three graph classification datasets, RC-Explainer achieves better or comparable performance to SOTA approaches w.r.t. predictive accuracy and contrastivity, and safely passes sanity checks and visual inspections. Codes are available at https://github.com/xiangwang1223/reinforced_causal_explainer.  ( 2 min )

Many real-world ubiquitous applications, such as parking recommendations and air pollution monitoring, benefit significantly from accurate long-term spatio-temporal forecasting (LSTF). LSTF makes use of long-term dependency between spatial and temporal domains, contextual information, and inherent pattern in the data. Recent studies have revealed the potential of multi-graph neural networks (MGNNs) to improve prediction performance. However, existing MGNN methods cannot be directly applied to LSTF due to several issues: the low level of generality, insufficient use of contextual information, and the imbalanced graph fusion approach. To address these issues, we construct new graph models to represent the contextual information of each node and the long-term spatio-temporal data dependency structure. To fuse the information across multiple graphs, we propose a new dynamic multi-graph fusion module to characterize the correlations of nodes within a graph and the nodes across graphs via the spatial attention and graph attention mechanisms. Furthermore, we introduce a trainable weight tensor to indicate the importance of each node in different graphs. Extensive experiments on two large-scale datasets demonstrate that our proposed approaches significantly improve the performance of existing graph neural network models in LSTF prediction tasks.  ( 2 min )

We study algorithms for approximating pairwise similarity matrices that arise in natural language processing. Generally, computing a similarity matrix for $n$ data points requires $\Omega(n^2)$ similarity computations. This quadratic scaling is a significant bottleneck, especially when similarities are computed via expensive functions, e.g., via transformer models. Approximation methods reduce this quadratic complexity, often by using a small subset of exactly computed similarities to approximate the remainder of the complete pairwise similarity matrix. Significant work focuses on the efficient approximation of positive semidefinite (PSD) similarity matrices, which arise e.g., in kernel methods. However, much less is understood about indefinite (non-PSD) similarity matrices, which often arise in NLP. Motivated by the observation that many of these matrices are still somewhat close to PSD, we introduce a generalization of the popular Nystr\"{o}m method to the indefinite setting. Our algorithm can be applied to any similarity matrix and runs in sublinear time in the size of the matrix, producing a rank-$s$ approximation with just $O(ns)$ similarity computations. We show that our method, along with a simple variant of CUR decomposition, performs very well in approximating a variety of similarity matrices arising in NLP tasks. We demonstrate high accuracy of the approximated similarity matrices in the downstream tasks of document classification, sentence similarity, and cross-document coreference.  ( 2 min )

Using the pre-trained language model (i.e. BERT) to apprehend source codes has attracted increasing attention from financial institutions owing to the great potential to uncover financial risks. However, there are several challenges in applying these language models to directly solve programming language (PL) related problems. To this end, we propose the AstBERT model, a pre-trained language model aiming to better understand the financial PL using the abstract syntax tree (AST). Specifically, we collect a colossal amount of source codes (both Java and Python) from the Alipay code repository and incorporate both syntactic and semantic code knowledge into our model through the help of code parsers, in which AST information of the source codes can be interpreted and integrated. We evaluate the performance of the proposed model on three tasks, including code question answering, code clone detection and code refinement. Experiment results show that our AstBERT achieves promising performance on three downstream tasks.  ( 2 min )

Renovating the memories in old photos is an intriguing research topic in computer vision fields. These legacy images often suffer from severe and commingled degradations such as cracks, noise, and color-fading, while lack of large-scale paired old photo datasets makes this restoration task very challenging. In this work, we present a novel reference-based end-to-end learning framework that can jointly repair and colorize the degraded legacy pictures. Specifically, the proposed framework consists of three modules: a restoration sub-network for degradation restoration, a similarity sub-network for color histogram matching and transfer, and a colorization subnet that learns to predict the chroma elements of the images conditioned on chromatic reference signals. The whole system takes advantage of the color histogram priors in a given reference image, which vastly reduces the dependency on large-scale training data. Apart from the proposed method, we also create, to our knowledge, the first public and real-world old photo dataset with paired ground truth for evaluating old photo restoration models, wherein each old photo is paired with a manually restored pristine image by PhotoShop experts. Our extensive experiments conducted on both synthetic and real-world datasets demonstrate that our method significantly outperforms state-of-the-arts both quantitatively and qualitatively.  ( 2 min )

In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not require any supervised data for training. Specifically, we first present Iterative Contrastive Learning (ICoL) that iteratively trains the query and document encoders with a cache mechanism. ICoL not only enlarges the number of negative instances but also keeps representations of cached examples in the same hidden space. We then propose Lexicon-Enhanced Dense Retrieval (LEDR) as a simple yet effective way to enhance dense retrieval with lexical matching. We evaluate LaPraDoR on the recently proposed BEIR benchmark, including 18 datasets of 9 zero-shot text retrieval tasks. Experimental results show that LaPraDoR achieves state-of-the-art performance compared with supervised dense retrieval models, and further analysis reveals the effectiveness of our training strategy and objectives. Compared to re-ranking, our lexicon-enhanced approach can be run in milliseconds (22.5x faster) while achieving superior performance.  ( 2 min )

Alternating gradient-descent-ascent (AltGDA) is an optimization algorithm that has been widely used for model training in various machine learning applications, which aims to solve a nonconvex minimax optimization problem. However, the existing studies show that it suffers from a high computation complexity in nonconvex minimax optimization. In this paper, we develop a single-loop and fast AltGDA-type algorithm that leverages proximal gradient updates and momentum acceleration to solve regularized nonconvex minimax optimization problems. By leveraging the momentum acceleration technique, we prove that the algorithm converges to a critical point in nonconvex minimax optimization and achieves a computation complexity in the order of $\mathcal{O}(\kappa^{\frac{11}{6}}\epsilon^{-2})$, where $\epsilon$ is the desired level of accuracy and $\kappa$ is the problem's condition number. {Such a computation complexity improves the state-of-the-art complexities of single-loop GDA and AltGDA algorithms (see the summary of comparison in \Cref{table1})}. We demonstrate the effectiveness of our algorithm via an experiment on adversarial deep learning.  ( 2 min )

VoiceFilter-Lite is a speaker-conditioned voice separation model that plays a crucial role in improving speech recognition and speaker verification by suppressing overlapping speech from non-target speakers. However, one limitation of VoiceFilter-Lite, and other speaker-conditioned speech models in general, is that these models are usually limited to a single target speaker. This is undesirable as most smart home devices now support multiple enrolled users. In order to extend the benefits of personalization to multiple users, we previously developed an attention-based speaker selection mechanism and applied it to VoiceFilter-Lite. However, the original multi-user VoiceFilter-Lite model suffers from significant performance degradation compared with single-user models. In this paper, we devised a series of experiments to improve the multi-user VoiceFilter-Lite model. By incorporating a dual learning rate schedule and by using feature-wise linear modulation (FiLM) to condition the model with the attended speaker embedding, we successfully closed the performance gap between multi-user and single-user VoiceFilter-Lite models on single-speaker evaluations. At the same time, the new model can also be easily extended to support any number of users, and significantly outperforms our previously published model on multi-speaker evaluations.  ( 2 min )

Low rank matrix recovery problems, including matrix completion and matrix sensing, appear in a broad range of applications. In this work we present GNMR -- an extremely simple iterative algorithm for low rank matrix recovery, based on a Gauss-Newton linearization. On the theoretical front, we derive recovery guarantees for GNMR in both the matrix sensing and matrix completion settings. Some of these results improve upon the best currently known for other methods. A key property of GNMR is that it implicitly keeps the factor matrices approximately balanced throughout its iterations. On the empirical front, we show that for matrix completion with uniform sampling, GNMR performs better than several popular methods, especially when given very few observations close to the information limit.  ( 2 min )

Stochastic gradient descent ascent (SGDA) and its variants have been the workhorse for solving minimax problems. However, in contrast to the well-studied stochastic gradient descent (SGD) with differential privacy (DP) constraints, there is little work on understanding the generalization (utility) of SGDA with DP constraints. In this paper, we use the algorithmic stability approach to establish the generalization (utility) of DP-SGDA in different settings. In particular, for the convex-concave setting, we prove that the DP-SGDA can achieve an optimal utility rate in terms of the weak primal-dual population risk in both smooth and non-smooth cases. To our best knowledge, this is the first-ever-known result for DP-SGDA in the non-smooth case. We further provide its utility analysis in the nonconvex-strongly-concave setting which is the first-ever-known result in terms of the primal population risk. The convergence and generalization results for this nonconvex setting are new even in the non-private setting. Finally, numerical experiments are conducted to demonstrate the effectiveness of DP-SGDA for both convex and nonconvex cases.  ( 2 min )

This work proposes a novel deep neural network (DNN) architecture, Implicit Segmentation Neural Network (ISNet), to solve the task of image segmentation followed by classification. It substitutes the common pipeline of two DNNs with a single model. We designed the ISNet for high flexibility and performance: it allows virtually any classification neural network architecture to analyze a common image as if it had been previously segmented. Furthermore, in relation to the unmodified classifier, the ISNet does not cause any increment in computational cost at run-time. We test the architecture with two applications: COVID-19 detection in chest X-rays, and facial attribute estimation. We implement an ISNet based on a DenseNet121 classifier, and compare the model to a U-net (performing lung/face segmentation) followed by a DenseNet121, and to a standalone DenseNet121. The new architecture matched the other DNNs in facial attribute estimation. Moreover, it strongly surpassed them in COVID-19 detection, according to an external test dataset. The ISNet precisely ignored the image regions outside of the lungs or faces. Therefore, in COVID-19 detection it reduced the effects of background bias and shortcut learning, and it improved security in facial attribute estimation. ISNet presents an accurate, fast, and light methodology. The successful implicit segmentation, considering two largely diverse fields, highlights the architecture's general applicability.  ( 3 min )

Recent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document. These methods do not require the exact document to be known during training and rely on the use of a retrieval system to fetch relevant documents from a large index. The documents used to generate the responses are modeled as latent variables whose prior probabilities need to be estimated. Models such as RAG and REALM, marginalize the document probabilities over the documents retrieved from the index to define the log likelihood loss function which is optimized end-to-end. In this paper, we develop a variational approach to the above technique wherein, we instead maximize the Evidence Lower bound (ELBO). Using a collection of three publicly available open-conversation datasets, we demonstrate how the posterior distribution, that has information from the ground-truth response, allows for a better approximation of the objective function during training. To overcome the challenges associated with sampling over a large knowledge collection, we develop an efficient approach to approximate the ELBO. To the best of our knowledge we are the first to apply variational training for open-scale unsupervised knowledge grounded dialog systems.  ( 2 min )

There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements. Often sophisticated reconstruction algorithms are deployed to maintain high image quality in such settings. In this work, we propose a data-driven sampler using a convolutional neural network, MNet, to provide object-specific sampling patterns adaptive to each scanned object. The network observes very limited low-frequency k-space data for each object and rapidly predicts the desired undersampling pattern in one go that achieves high image reconstruction quality. We propose an accompanying alternating-type training framework with a mask-backward procedure that efficiently generates training labels for the sampler network and jointly trains an image reconstruction network. Experimental results on the fastMRI knee dataset demonstrate the ability of the proposed learned undersampling network to generate object-specific masks at fourfold and eightfold acceleration that achieve superior image reconstruction performance than several existing schemes. The source code for the proposed joint sampling and reconstruction learning framework is available at https://github.com/zhishenhuang/mri.  ( 2 min )

It has been widely documented that the sampling and resampling steps in particle filters cannot be differentiated. The {\itshape reparameterisation trick} was introduced to allow the sampling step to be reformulated into a differentiable function. We extend the {\itshape reparameterisation trick} to include the stochastic input to resampling therefore limiting the discontinuities in the gradient calculation after this step. Knowing the gradients of the prior and likelihood allows us to run particle Markov Chain Monte Carlo (p-MCMC) and use the No-U-Turn Sampler (NUTS) as the proposal when estimating parameters. We compare the Metropolis-adjusted Langevin algorithm (MALA), Hamiltonian Monte Carlo with different number of steps and NUTS. We consider two state-space models and show that NUTS improves the mixing of the Markov chain and can produce more accurate results in less computational time.  ( 2 min )

Finding the closest separable state to a given target state is a notoriously difficult task, even more difficult than deciding whether a state is entangled or separable. To tackle this task, we parametrize separable states with a neural network and train it to minimize the distance to a given target state, with respect to a differentiable distance, such as the trace distance or Hilbert--Schmidt distance. By examining the output of the algorithm, we obtain an upper bound on the entanglement of the target state, and construct an approximation for its closest separable state. We benchmark the method on a variety of well-known classes of bipartite states and find excellent agreement, even up to local dimension of $d=10$, while providing conjectures and analytic insight for isotropic and Werner states. Moreover, we show our method to be efficient in the multipartite case, considering different notions of separability. Examining three and four-party GHZ and W states we recover known bounds and obtain novel ones, for instance for triseparability.  ( 2 min )

Obtaining annotations for large training sets is expensive, especially in settings where domain knowledge is required, such as behavior analysis. Weak supervision has been studied to reduce annotation costs by using weak labels from task-specific labeling functions (LFs) to augment ground truth labels. However, domain experts still need to hand-craft different LFs for different tasks, limiting scalability. To reduce expert effort, we present AutoSWAP: a framework for automatically synthesizing data-efficient task-level LFs. The key to our approach is to efficiently represent expert knowledge in a reusable domain-specific language and more general domain-level LFs, with which we use state-of-the-art program synthesis techniques and a small labeled dataset to generate task-level LFs. Additionally, we propose a novel structural diversity cost that allows for efficient synthesis of diverse sets of LFs, further improving AutoSWAP's performance. We evaluate AutoSWAP in three behavior analysis domains and demonstrate that AutoSWAP outperforms existing approaches using only a fraction of the data. Our results suggest that AutoSWAP is an effective way to automatically generate LFs that can significantly reduce expert effort for behavior analysis.  ( 2 min )

We present a Newton-type method that converges fast from any initialization and for arbitrary convex objectives with Lipschitz Hessians. We achieve this by merging the ideas of cubic regularization with a certain adaptive Levenberg--Marquardt penalty. In particular, we show that the iterates given by $x^{k+1}=x^k - \bigl(\nabla^2 f(x^k) + \sqrt{H\|\nabla f(x^k)\|} \mathbf{I}\bigr)^{-1}\nabla f(x^k)$, where $H>0$ is a constant, converge globally with a $\mathcal{O}(\frac{1}{k^2})$ rate. Our method is the first variant of Newton's method that has both cheap iterations and provably fast global convergence. Moreover, we prove that locally our method converges superlinearly when the objective is strongly convex. To boost the method's performance, we present a line search procedure that does not need hyperparameters and is provably efficient.  ( 2 min )

Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learned models could prove beneficial for accurate density reconstruction particularly in dynamic imaging, where the time-evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. We use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time-series) from simulations. A supervised loss is also included in the training, which leads to improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.  ( 2 min )

The online spreading of fake news is a major issue threatening entire societies. Much of this spreading is enabled by new media formats, namely social networks and online media sites. Researchers and practitioners have been trying to answer this by characterizing the fake news and devising automated methods for detecting them. The detection methods had so far only limited success, mostly due to the complexity of the news content and context and lack of properly annotated datasets. One possible way to boost the efficiency of automated misinformation detection methods, is to imitate the detection work of humans. It is also important to understand the news consumption behavior of online users. In this paper, we present an eye-tracking study, in which we let 44 lay participants to casually read through a social media feed containing posts with news articles, some of which were fake. In a second run, we asked the participants to decide on the truthfulness of these articles. We also describe a follow-up qualitative study with a similar scenario but this time with 7 expert fake news annotators. We present the description of both studies, characteristics of the resulting dataset (which we hereby publish) and several findings.  ( 2 min )

In certain situations, neural networks are trained upon data that obey underlying symmetries. However, the predictions do not respect the symmetries exactly unless embedded in the network structure. In this work, we introduce architectures that embed a special kind of symmetry namely, invariance with respect to involutory linear/affine transformations up to parity $p=\pm 1$. We provide rigorous theorems to show that the proposed network ensures such an invariance and present qualitative arguments for a special universal approximation theorem. An adaption of our techniques to CNN tasks for datasets with inherent horizontal/vertical reflection symmetry is demonstrated. Extensive experiments indicate that the proposed model outperforms baseline feed-forward and physics-informed neural networks while identically respecting the underlying symmetry.  ( 2 min )

In light of the COVID-19 pandemic, it is an open challenge and critical practical problem to find a optimal way to dynamically prescribe the best policies that balance both the governmental resources and epidemic control in different countries and regions. To solve this multi-dimensional tradeoff of exploitation and exploration, we formulate this technical challenge as a contextual combinatorial bandit problem that jointly optimizes a multi-criteria reward function. Given the historical daily cases in a region and the past intervention plans in place, the agent should generate useful intervention plans that policy makers can implement in real time to minimizing both the number of daily COVID-19 cases and the stringency of the recommended interventions. We prove this concept with simulations of multiple realistic policy making scenarios and demonstrate a clear advantage in providing a pareto optimal solution in the epidemic intervention problem.  ( 2 min )

Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN) neural networks have also been investigated, in effort to exploit topological information within the power grid. Although promising results have been obtained, there lacks a systematic comparison between these architectures throughout literature. Accordingly, we introduce a concise framework for generalizing methods for machine learning assisted OPF and assess the performance of a variety of FCNN, CNN and GNN models for two fundamental approaches in this domain: regression (predicting optimal generator set-points) and classification (predicting the active set of constraints). For several synthetic power grids with interconnected utilities, we show that locality properties between feature and target variables are scarce and subsequently demonstrate marginal utility of applying CNN and GNN architectures compared to FCNN for a fixed grid topology. However, with variable topology (for instance, modeling transmission line contingency), GNN models are able to straightforwardly take the change of topological information into account and outperform both FCNN and CNN models.  ( 2 min )

In recent years, there have been many deep structures for Reinforcement Learning, mainly for value function estimation and representations. These methods achieved great success in Atari 2600 domain. In this paper, we propose an improved architecture based upon Dueling Networks, in this architecture, there are two separate estimators, one approximate the state value function and the other, state advantage function. This improvement based on Maximum Entropy, shows better policy evaluation compared to the original network and other value-based architectures in Atari domain.  ( 2 min )

In real-time forecasting in public health, data collection is a non-trivial and demanding task. Often after initially released, it undergoes several revisions later (maybe due to human or technical constraints) - as a result, it may take weeks until the data reaches to a stable value. This so-called 'backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. In this paper, we introduce the multi-variate backfill problem using COVID-19 as the motivating example. We construct a detailed dataset composed of relevant signals over the past year of the pandemic. We then systematically characterize several patterns in backfill dynamics and leverage our observations for formulating a novel problem and neural framework Back2Future that aims to refines a given model's predictions in real-time. Our extensive experiments demonstrate that our method refines the performance of top models for COVID-19 forecasting, in contrast to non-trivial baselines, yielding 18% improvement over baselines, enabling us obtain a new SOTA performance. In addition, we show that our model improves model evaluation too; hence policy-makers can better understand the true accuracy of forecasting models in real-time.  ( 3 min )

Unsupervised user adaptation aligns the feature distributions of the data from training users and the new user, so a well-trained wearable human activity recognition (WHAR) model can be well adapted to the new user. With the development of wearable sensors, multiple wearable sensors based WHAR is gaining more and more attention. In order to address the challenge that the transferabilities of different sensors are different, we propose SALIENCE (unsupervised user adaptation model for multiple wearable sensors based human activity recognition) model. It aligns the data of each sensor separately to achieve local alignment, while uniformly aligning the data of all sensors to ensure global alignment. In addition, an attention mechanism is proposed to focus the activity classifier of SALIENCE on the sensors with strong feature discrimination and well distribution alignment. Experiments are conducted on two public WHAR datasets, and the experimental results show that our model can yield a competitive performance.  ( 2 min )

In this work, we formulate a visual dialog as an information flow in which each piece of information is encoded with the joint visual-linguistic representation of a single dialog round. Based on this formulation, we consider the visual dialog task as a sequence problem consisting of ordered visual-linguistic vectors. For featurization, we use a Dense Symmetric Co-Attention network as a lightweight vison-language joint representation generator to fuse multimodal features (i.e., image and text), yielding better computation and data efficiencies. For inference, we propose two Sequential Dialog Networks (SeqDialN): the first uses LSTM for information propagation (IP) and the second uses a modified Transformer for multi-step reasoning (MR). Our architecture separates the complexity of multimodal feature fusion from that of inference, which allows simpler design of the inference engine. IP based SeqDialN is our baseline with a simple 2-layer LSTM design that achieves decent performance. MR based SeqDialN, on the other hand, recurrently refines the semantic question/history representations through the self-attention stack of Transformer and produces promising results on the visual dialog task. On VisDial v1.0 test-std dataset, our best single generative SeqDialN achieves 62.54% NDCG and 48.63% MRR; our ensemble generative SeqDialN achieves 63.78% NDCG and 49.98% MRR, which set a new state-of-the-art generative visual dialog model. We fine-tune discriminative SeqDialN with dense annotations and boost the performance up to 72.41% NDCG and 55.11% MRR. In this work, we discuss the extensive experiments we have conducted to demonstrate the effectiveness of our model components. We also provide visualization for the reasoning process from the relevant conversation rounds and discuss our fine-tuning methods. Our code is available at https://github.com/xiaoxiaoheimei/SeqDialN  ( 3 min )

Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, particularly those represented as discrete density maps. However, the optimization of such cellular structures is challenging due to the multiscale design problem. Past work addressing this problem generally either only optimizes the volume fraction of single-type unit cells but ignores the effects of unit cell geometry on properties, or considers the geometry-property relation but builds this relation via heuristics. In contrast, we propose a simple yet more principled way to accurately model the property to geometry mapping using a conditional deep generative model, named Inverse Homogenization Generative Adversarial Network (IH-GAN). It learns the conditional distribution of unit cell geometries given properties and can realize the one-to-many mapping from properties to geometries. We further reduce the complexity of IH-GAN by using the implicit function parameterization to represent unit cell geometries. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy ($R^2$-scores between target properties and properties of generated unit cells $>98\%$) and 2) improve the optimized structural performance over the conventional variable-density single-type structure. In the minimum compliance example, our IH-GAN generated structure achieves a $79.7\%$ reduction in concentrated stress and an extra $3.03\%$ reduction in displacement. In the target deformation examples, our IH-GAN generated structure reduces the target matching error by $86.4\%$ and $79.6\%$ for two test cases, respectively. We also demonstrated that the connectivity issue for multi-type unit cells can be solved by transition layer blending.  ( 3 min )

Knowledge graph (KG), integrating complex information and containing rich semantics, is widely considered as side information to enhance the recommendation systems. However, most of the existing KG-based methods concentrate on encoding the structural information in the graph, without utilizing the collaborative signals in user-item interaction data, which are important for understanding user preferences. Therefore, the representations learned by these models are insufficient for representing semantic information of users and items in the recommendation environment. The combination of both kinds of data provides a good chance to solve this problem. To tackle this research gap, we propose a novel duet representation learning framework named \sysname to fuse local information (user-item interaction data) and global information (external knowledge graph) for the top-$N$ recommendation, which is composed of two separate sub-models. One learns the local representations by discovering the inner correlations in local information with a knowledge-aware co-attention mechanism, and another learns the global representations by encoding the knowledge associations in global information with a relation-aware attention network. The two sub-models are jointly trained as part of the semantic fusion network to compute the user preferences, which discriminates the contribution of the two sub-models under the special context. We conduct experiments on two real-world datasets, and the evaluations show that KADM significantly outperforms state-of-art methods. Further ablation studies confirm that the duet architecture performs significantly better than either sub-model on the recommendation tasks.  ( 2 min )

In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and Hinf-norm based correction for a linear Gaussian system. As the dimension of state or parameter space grows, performing the full Kalman update with the dense covariance matrix for a large scale system requires increased storage and computational complexity, making it impractical. The VIF approach, based on mean-field Gaussian variational inference, reduces this burden through the variational approximation to the covariance usually in the form of a diagonal covariance approximation. The challenge is to retain convergence and correct for biases introduced by the sequential VIF steps. We desire a framework that improves feasibility while still maintaining reasonable proximity to the optimal Kalman filter as data is assimilated. To accomplish this goal, a Hinf-norm based optimization perturbs the VIF covariance matrix to improve robustness. This yields a novel VIF- Hinf recursion that employs consecutive variational inference and Hinf based optimization steps. We explore the development of this method and investigate a numerical example to illustrate the effectiveness of the proposed filter.  ( 2 min )

This paper makes two scientific contributions to the field of exoskeleton-based action and movement recognition. First, it presents a novel machine learning and pattern recognition-based framework that can detect a wide range of actions and movements - walking, walking upstairs, walking downstairs, sitting, standing, lying, stand to sit, sit to stand, sit to lie, lie to sit, stand to lie, and lie to stand, with an overall accuracy of 82.63%. Second, it presents a comprehensive comparative study of different learning approaches - Random Forest, Artificial Neural Network, Decision Tree, Multiway Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Decision Stump, AutoMLP, Linear Regression, Vector Linear Regression, Random Tree, Na\"ive Bayes, Na\"ive Bayes (Kernel), Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Deep Learning applied to this framework. The performance of each of these learning approaches was boosted by using the AdaBoost algorithm, and the Cross Validation approach was used for training and testing. The results show that in boosted form, the k-NN classifier outperforms all the other boosted learning approaches and is, therefore, the optimal learning method for this purpose. The results presented and discussed uphold the importance of this work to contribute towards augmenting the abilities of exoskeleton-based assisted and independent living of the elderly in the future of Internet of Things-based living environments, such as Smart Homes. As a specific use case, we also discuss how the findings of our work are relevant for augmenting the capabilities of the Hybrid Assistive Limb exoskeleton, a highly functional lower limb exoskeleton.  ( 2 min )

This paper is concerned with contrastive learning (CL) for low-level image restoration and enhancement tasks. We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs. While supervised image reconstruction aims to minimize residual terms directly, RCL alternatively builds a connection between residuals and CL by defining a novel instance discrimination pretext task, using residuals as the discriminative feature. Our formulation mitigates the severe task misalignment between instance discrimination pretext tasks and downstream image reconstruction tasks, present in existing CL frameworks. Experimentally, we find that RCL can learn robust and transferable representations that improve the performance of various downstream tasks, such as denoising and super resolution, in comparison with recent self-supervised methods designed specifically for noisy inputs. Additionally, our unsupervised pre-training can significantly reduce annotation costs whilst maintaining performance competitive with fully-supervised image reconstruction.  ( 2 min )

We propose the neural string edit distance model for string-pair matching and string transduction based on learnable string edit distance. We modify the original expectation-maximization learned edit distance algorithm into a differentiable loss function, allowing us to integrate it into a neural network providing a contextual representation of the input. We evaluate on cognate detection, transliteration, and grapheme-to-phoneme conversion, and show that we can trade off between performance and interpretability in a single framework. Using contextual representations, which are difficult to interpret, we match the performance of state-of-the-art string-pair matching models. Using static embeddings and a slightly different loss function, we force interpretability, at the expense of an accuracy drop.  ( 2 min )

Personalization methods in federated learning aim to balance the benefits of federated and local training for data availability, communication cost, and robustness to client heterogeneity. Approaches that require clients to communicate all model parameters can be undesirable due to privacy and communication constraints. Other approaches require always-available or stateful clients, impractical in large-scale cross-device settings. We introduce Federated Reconstruction, the first model-agnostic framework for partially local federated learning suitable for training and inference at scale. We motivate the framework via a connection to model-agnostic meta learning, empirically demonstrate its performance over existing approaches for collaborative filtering and next word prediction, and release an open-source library for evaluating approaches in this setting. We also describe the successful deployment of this approach at scale for federated collaborative filtering in a mobile keyboard application.  ( 2 min )

Graph Contrastive Learning (GCL) has proven highly effective in promoting the performance of Semi-Supervised Node Classification (SSNC). However, existing GCL methods are generally transferred from other fields like CV or NLP, whose underlying working mechanism remains under-explored. In this work, we first deeply probe the working mechanism of GCL in SSNC, and find that the promotion brought by GCL is severely unevenly distributed: the improvement mainly comes from subgraphs with less annotated information, which is fundamentally different from contrastive learning in other fields. However, existing GCL methods generally ignore this uneven distribution of annotated information and apply GCL evenly to the whole graph. To remedy this issue and further improve GCL in SSNC, we propose the Topology InFormation gain-Aware Graph Contrastive Learning (TIFA-GCL) framework that considers the annotated information distribution across graph in GCL. Extensive experiments on six benchmark graph datasets, including the enormous OGB-Products graph, show that TIFA-GCL can bring a larger improvement than existing GCL methods in both transductive and inductive settings. Further experiments demonstrate the generalizability and interpretability of TIFA-GCL.  ( 2 min )

Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate change. Recent developments in the field of causal machine learning enable the estimation of intervention impacts on an outcome of interest, for samples described by a set of observed characteristics. We introduce an extensible data-driven framework that leverages earth observations and frames agricultural land suitability as a geospatial impact assessment problem, where the estimated effects of agricultural practices on agroecosystems serve as a land suitability score and guide decision making. We formulate this as a causal machine learning task and discuss how this approach can be used for agricultural planning in a changing climate. Specifically, we extract the agricultural management practices of "crop rotation" and "landscape crop diversity" from crop type maps, account for climate and land use data, and use double machine learning to estimate their heterogeneous effect on Net Primary Productivity (NPP), within the Flanders region of Belgium from 2010 to 2020. We find that the effect of crop rotation was insignificant, while landscape crop diversity had a small negative effect on NPP. Finally, we observe considerable effect heterogeneity in space for both practices and analyze it.  ( 2 min )

Consider the following distributed optimization scenario. A worker has access to training data that it uses to compute the gradients while a server decides when to stop iterative computation based on its target accuracy or delay constraints. The server receives all its information about the problem instance from the worker via a rate-limited noiseless communication channel. We introduce the principle we call Differential Quantization (DQ) that prescribes compensating the past quantization errors to direct the descent trajectory of a quantized algorithm towards that of its unquantized counterpart. Assuming that the objective function is smooth and strongly convex, we prove that Differentially Quantized Gradient Descent (DQ-GD) attains a linear contraction factor of $\max\{\sigma_{\mathrm{GD}}, \rho_n 2^{-R}\}$, where $\sigma_{\mathrm{GD}}$ is the contraction factor of unquantized gradient descent (GD), $\rho_n \geq 1$ is the covering efficiency of the quantizer, and $R$ is the bitrate per problem dimension $n$. Thus at any $R\geq\log_2 \rho_n /\sigma_{\mathrm{GD}}$ bits, the contraction factor of DQ-GD is the same as that of unquantized GD, i.e., there is no loss due to quantization. We show that no algorithm within a certain class can converge faster than $\max\{\sigma_{\mathrm{GD}}, 2^{-R}\}$. Since quantizers exist with $\rho_n \to 1$ as $n \to \infty$ (Rogers, 1963), this means that DQ-GD is asymptotically optimal. The principle of differential quantization continues to apply to gradient methods with momentum such as Nesterov's accelerated gradient descent, and Polyak's heavy ball method. For these algorithms as well, if the rate is above a certain threshold, there is no loss in contraction factor obtained by the differentially quantized algorithm compared to its unquantized counterpart. Experimental results on least-squares problems validate our theoretical analysis.  ( 3 min )

Data-driven analysis of complex networks has been in the focus of research for decades. An important area of research is to study how well real networks can be described with a small selection of metrics, furthermore how well network models can capture the relations between graph metrics observed in real networks. In this paper, we apply machine learning techniques to investigate the aforementioned problems. We study 500 real-world networks along with 2,000 synthetic networks generated by four frequently used network models with previously calibrated parameters to make the generated graphs as similar to the real networks as possible. This paper unifies several branches of data-driven complex network analysis, such as the study of graph metrics and their pair-wise relationships, network similarity estimation, model calibration, and graph classification. We find that the correlation profiles of the structural measures significantly differ across network domains and the domain can be efficiently determined using a small selection of graph metrics. The structural properties of the network models with fixed parameters are robust enough to perform parameter calibration. The goodness-of-fit of the network models highly depends on the network domain. By solving classification problems, we find that the models lack the capability of generating a graph with a high clustering coefficient and relatively large diameter simultaneously. On the other hand, models are able to capture exactly the degree-distribution-related metrics.  ( 2 min )

As the pump-and-dump schemes (P&Ds) proliferate in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance, to inform potentially susceptible investors before they become victims. In this paper, we focus on the target coin prediction task, i.e., to predict the pump probability of all coins listed in the target exchange before a pump. We conduct a comprehensive study of the latest P&Ds, investigate 709 events organized in Telegram channels from Jan. 2019 to Jan. 2022, and unearth some abnormal yet interesting patterns of P&Ds. Empirical analysis demonstrates that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity, which inspires us to develop a novel sequence-based neural network named SNN. Specifically, SNN encodes each channel's pump history as a sequence representation via a positional attention mechanism, which filters useful information and alleviates the noise introduced when the sequence length is long. We also identify and address the coin-side cold-start problem in a practical setting. Extensive experiments show a lift of 1.6% AUC and 41.0% Hit Ratio@3 brought by our method, making it well-suited for real-world application. As a side contribution, we release the source code of our entire data science pipeline on GitHub, along with the dataset tailored for studying the latest P&Ds.  ( 2 min )

Allocation strategies improve the efficiency of crowdsourcing by decreasing the work needed to complete individual tasks accurately. However, these algorithms introduce bias by preferentially allocating workers onto easy tasks, leading to sets of completed tasks that are no longer representative of all tasks. This bias challenges inference of problem-wide properties such as typical task difficulty or crowd properties such as worker completion times, important information that goes beyond the crowd responses themselves. Here we study inference about problem properties when using an allocation algorithm to improve crowd efficiency. We introduce Decision-Explicit Probability Sampling (DEPS), a novel method to perform inference of problem properties while accounting for the potential bias introduced by an allocation strategy. Experiments on real and synthetic crowdsourcing data show that DEPS outperforms baseline inference methods while still leveraging the efficiency gains of the allocation method. The ability to perform accurate inference of general properties when using non-representative data allows crowdsourcers to extract more knowledge out of a given crowdsourced dataset.  ( 2 min )

In this paper, we present a methodology for fisheries-related data that allows us to converge on a labeled image dataset by iterating over the dataset with multiple training and production loops that can exploit crowdsourcing interfaces. We present our algorithm and its results on two separate sets of image data collected using the Seabed autonomous underwater vehicle. The first dataset comprises of 2,026 completely unlabeled images, while the second consists of 21,968 images that were point annotated by experts. Our results indicate that training with a small subset and iterating on that to build a larger set of labeled data allows us to converge to a fully annotated dataset with a small number of iterations. Even in the case of a dataset labeled by experts, a single iteration of the methodology improves the labels by discovering additional complicated examples of labels associated with fish that overlap, are very small, or obscured by the contrast limitations associated with underwater imagery.  ( 2 min )

Crowdsourcing is an online outsourcing mode which can solve the current machine learning algorithm's urge need for massive labeled data. Requester posts tasks on crowdsourcing platforms, which employ online workers over the Internet to complete tasks, then aggregate and return results to requester. How to model the interaction between different types of workers and tasks is a hot spot. In this paper, we try to model workers as four types based on their ability: expert, normal worker, sloppy worker and spammer, and divide tasks into hard, medium and easy task according to their difficulty. We believe that even experts struggle with difficult tasks while sloppy workers can get easy tasks right, and spammers always give out wrong answers deliberately. So, good examination tasks should have moderate degree of difficulty and discriminability to score workers more objectively. Thus, we first score workers' ability mainly on the medium difficult tasks, then reducing the weight of answers from sloppy workers and modifying the answers from spammers when inferring the tasks' ground truth. A probability graph model is adopted to simulate the task execution process, and an iterative method is adopted to calculate and update the ground truth, the ability of workers and the difficulty of the task successively. We verify the rightness and effectiveness of our algorithm both in simulated and real crowdsourcing scenes.  ( 2 min )

In this paper we investigate the correlation between NFT valuations and various features from three primary categories: public market data, NFT metadata, and social trends data.  ( 2 min )

Building generalizable goal-conditioned agents from rich observations is a key to reinforcement learning (RL) solving real world problems. Traditionally in goal-conditioned RL, an agent is provided with the exact goal they intend to reach. However, it is often not realistic to know the configuration of the goal before performing a task. A more scalable framework would allow us to provide the agent with an example of an analogous task, and have the agent then infer what the goal should be for its current state. We propose a new form of state abstraction called goal-conditioned bisimulation that captures functional equivariance, allowing for the reuse of skills to achieve new goals. We learn this representation using a metric form of this abstraction, and show its ability to generalize to new goals in simulation manipulation tasks. Further, we prove that this learned representation is sufficient not only for goal conditioned tasks, but is amenable to any downstream task described by a state-only reward function. Videos can be found at https://sites.google.com/view/gc-bisimulation.  ( 2 min )

We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998), nature is often modeled as an adaptive adversary who sees all activity of the forecaster except the randomization that the forecaster may deploy. A number of papers have proposed randomized forecasting strategies that achieve an $\epsilon$-calibration error rate of $O(1/\sqrt{T})$, which we prove is tight in general. On the other hand, it is well known that it is not possible to be calibrated without randomization, or if nature also sees the forecaster's randomization; in both cases the calibration error could be $\Omega(1)$. Inspired by the equally seminal works on the "power of two choices" and imprecise probability theory, we study a small variant of the standard online calibration problem. The adversary gives the forecaster the option of making two nearby probabilistic forecasts, or equivalently an interval forecast of small width, and the endpoint closest to the revealed outcome is used to judge calibration. This power of two choices, or imprecise forecast, accords the forecaster with significant power -- we show that a faster $\epsilon$-calibration rate of $O(1/T)$ can be achieved even without deploying any randomization.  ( 2 min )

Humans have a remarkably large capacity to store detailed visual information in long-term memory even after a single exposure, as demonstrated by classic experiments in psychology. For example, Standing (1973) showed that humans could recognize with high accuracy thousands of pictures that they had seen only once a few days prior to a recognition test. In deep learning, the primary mode of incorporating new information into a model is through gradient descent in the model's parameter space. This paper asks whether deep learning via gradient descent can match the efficiency of human visual long-term memory to incorporate new information in a rigorous, head-to-head, quantitative comparison. We answer this in the negative: even in the best case, models learning via gradient descent appear to require approximately 10 exposures to the same visual materials in order to reach a recognition memory performance humans achieve after only a single exposure. Prior knowledge induced via pretraining and bigger model sizes improve performance, but these improvements are not very visible after a single exposure (it takes a few exposures for the improvements to become apparent), suggesting that simply scaling up the pretraining data size or model size might not be enough for the model to reach human-level memory efficiency.  ( 2 min )

We present NLU++, a novel dataset for natural language understanding (NLU) in task-oriented dialogue (ToD) systems, with the aim to provide a much more challenging evaluation environment for dialogue NLU models, up to date with the current application and industry requirements. NLU++ is divided into two domains (BANKING and HOTELS) and brings several crucial improvements over current commonly used NLU datasets. \textbf{1)} NLU++ provides fine-grained domain ontologies with a large set of challenging \textit{multi-intent} sentences, introducing and validating the idea of \textit{intent modules} that can be combined into complex intents that convey complex user goals, combined with finer-grained and thus more challenging slot sets. \textbf{2)} The ontology is divided into \textit{domain-specific} and \textit{generic} (i.e., domain-universal) intent modules that overlap across domains, promoting cross-domain reusability of annotated examples. \textbf{3)} The dataset design has been inspired by the problems observed in industrial ToD systems, and \textbf{4)} it has been collected, filtered and carefully annotated by dialogue NLU experts, yielding high-quality annotated data. Finally, we benchmark a series of current state-of-the-art NLU models on NLU++; the results demonstrate the challenging nature of the dataset, especially in low-data regimes, the validity of `intent modularisation', and call for further research on ToD NLU.  ( 2 min )

Dropout as regularization has been used extensively to prevent overfitting for training neural networks. During training, units and their connections are randomly dropped, which could be considered as sampling many different submodels from the original model. At test time, weight scaling and Monte Carlo approximation are two widely applied approaches to approximate the outputs. Both approaches work well practically when all submodels are low-bias complex learners. However, in this work, we demonstrate scenarios where some submodels behave closer to high-bias models and a non-uniform weight scaling is a better approximation for inference.  ( 2 min )

We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms. We propose two attention mechanisms for binding actions to objects, soft attention and hard attention, which we evaluate in the context of structured world models for five environments. Our experiments show that hard attention helps contrastively-trained structured world models to learn to separate individual objects in an object-based grid-world environment. Further, we show that soft attention increases performance of factored world models trained on a robotic manipulation task. The learned action attention weights can be used to interpret the factored world model as the attention focuses on the manipulated object in the environment.  ( 2 min )

Deep neural networks are known for their inability to learn robust representations when biases exist in the dataset. This results in a poor generalization to unbiased datasets, as the predictions strongly rely on peripheral and confounding factors, which are erroneously learned by the network. Many existing works deal with this issue by either employing an explicit supervision on the bias attributes, or assuming prior knowledge about the bias. In this work we study this problem in a more difficult scenario, in which no explicit annotation about the bias is available, and without any prior knowledge about its nature. We propose a fully unsupervised debiasing framework, consisting of three steps: first, we exploit the natural preference for learning malignant biases, obtaining a bias-capturing model; then, we perform a pseudo-labelling step to obtain bias labels; finally we employ state-of-the-art supervised debiasing techniques to obtain an unbiased model. We also propose a theoretical framework to assess the biasness of a model, and provide a detailed analysis on how biases affect the training of neural networks. We perform experiments on synthetic and real-world datasets, showing that our method achieves state-of-the-art performance in a variety of settings, sometimes even higher than fully supervised debiasing approaches.  ( 2 min )

Multi-agent reinforcement learning holds the key for solving complex tasks that demand the coordination of learning agents. However, strong coordination often leads to expensive exploration over the exponentially large state-action space. A powerful approach is to decompose team works into roles, which are ideally assigned to agents with the relevant skills. Training agents to adaptively choose and play emerging roles in a team thus allows the team to scale to complex tasks and quickly adapt to changing environments. These promises, however, have not been fully realised by current role-based multi-agent reinforcement learning methods as they assume either a pre-defined role structure or a fixed team size. We propose a framework to learn role assignment and transfer across team sizes. In particular, we train a role assignment network for small teams by demonstration and transfer the network to larger teams, which continue to learn through interaction with the environment. We demonstrate that re-using the role-based credit assignment structure can foster the learning process of larger reinforcement learning teams to achieve tasks requiring different roles. Our proposal outperforms competing techniques in enriched role-enforcing Prey-Predator games and in new scenarios in the StarCraft II Micro-Management benchmark.  ( 2 min )

While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model.  ( 2 min )

The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit parameter optimization at the pre-layout stage, i.e., finding device parameters to fulfill desired circuit specifications. Unlike all prior methods, our approach is inspired by human experts who rely on domain knowledge of analog circuit design (e.g., circuit topology and couplings between circuit specifications) to tackle the problem. By originally incorporating such key domain knowledge into policy training with a multimodal network, the method best learns the complex relations between circuit parameters and design targets, enabling optimal decisions in the optimization process. Experimental results on exemplary circuits show it achieves human-level design accuracy (99%) 1.5X efficiency of existing best-performing methods. Our method also shows better generalization ability to unseen specifications and optimality in circuit performance optimization. Moreover, it applies to design radio-frequency circuits on emerging semiconductor technologies, breaking the limitations of prior learning methods in designing conventional analog circuits.  ( 2 min )

Meshless methods are an active and modern branch of numerical analysis with many intriguing benefits. One of the main open research questions related to local meshless methods is how to select the best possible stencil - a collection of neighbouring nodes - to base the calculation on. In this paper, we describe the procedure for generating a labelled stencil dataset and use a variation of pointNet - a deep learning network based on point clouds - to create a classifier for the quality of the stencil. We exploit features of pointNet to implement a model that can be used to classify differently sized stencils and compare it against models dedicated to a single stencil size. The model is particularly good at detecting the best and the worst stencils with a respectable area under the curve (AUC) metric of around 0.90. There is much potential for further improvement and direct application in the meshless domain.  ( 2 min )

Code summarization with deep learning has been widely studied in recent years. Current deep learning models for code summarization generally follow the principle in neural machine translation and adopt the encoder-decoder framework, where the encoder learns the semantic representations from source code and the decoder transforms the learnt representations into human-readable text that describes the functionality of code snippets. Despite they achieve the new state-of-the-art performance, we notice that current models often either generate less fluent summaries, or fail to capture the core functionality, since they usually focus on a single type of code representations. As such we propose GypSum, a new deep learning model that learns hybrid representations using graph attention neural networks and a pre-trained programming and natural language model. We introduce particular edges related to the control flow of a code snippet into the abstract syntax tree for graph construction, and design two encoders to learn from the graph and the token sequence of source code, respectively. We modify the encoder-decoder sublayer in the Transformer's decoder to fuse the representations and propose a dual-copy mechanism to facilitate summary generation. Experimental results demonstrate the superior performance of GypSum over existing code summarization models.  ( 2 min )

A new algorithm based on bayesian inference for learning local graph conductance based on Gaussian Process(GP) is given that uses advanced MCMC convergence ideas to create a scalable and fast algorithm for convergence to stationary distribution which is provided to learn the bahavior of conductance when traversing the indirected weighted graph. First metric embedding is used to represent the vertices of the graph. Then, uniform induced conductance is calculated for training points. Finally, in the learning step, a gaussian process is used to approximate the uniform induced conductance. MCMC is used to measure uncertainty of estimated hyper-parameters.  ( 2 min )

The projected belief network (PBN) is a layered generative network (LGN) with tractable likelihood function, and is based on a feed-forward neural network (FFNN). There are two versions of the PBN: stochastic and deterministic (D-PBN), and each has theoretical advantages over other LGNs. However, implementation of the PBN requires an iterative algorithm that includes the inversion of a symmetric matrix of size M X M in each layer, where M is the layer output dimension. This, and the fact that the network must be always dimension-reducing in each layer, can limit the types of problems where the PBN can be applied. In this paper, we describe techniques to avoid or mitigate these restrictions and use the PBN effectively at high dimension. We apply the discriminatively aligned PBN (PBN-DA) to classifying and auto-encoding high-dimensional spectrograms of acoustic events. We also present the discriminatively aligned D-PBN for the first time.  ( 2 min )

Deep neural networks have been extremely successful as highly accurate wave function ans\"atze for variational Monte Carlo calculations of molecular ground states. We present an extension of one such ansatz, FermiNet, to calculations of the ground states of periodic Hamiltonians, and study the homogeneous electron gas. FermiNet calculations of the ground-state energies of small electron gas systems are in excellent agreement with previous initiator full configuration interaction quantum Monte Carlo and diffusion Monte Carlo calculations. We investigate the spin-polarized homogeneous electron gas and demonstrate that the same neural network architecture is capable of accurately representing both the delocalized Fermi liquid state and the localized Wigner crystal state. The network is given no \emph{a priori} knowledge that a phase transition exists, but converges on the translationally invariant ground state at high density and spontaneously breaks the symmetry to produce the crystalline ground state at low density.  ( 2 min )

Client selection strategies are widely adopted to handle the communication-efficient problem in recent studies of Federated Learning (FL). However, due to the large variance of the selected subset's update, prior selection approaches with a limited sampling ratio cannot perform well on convergence and accuracy in heterogeneous FL. To address this problem, in this paper, we propose a novel stratified client selection scheme to reduce the variance for the pursuit of better convergence and higher accuracy. Specifically, to mitigate the impact of heterogeneity, we develop stratification based on clients' local data distribution to derive approximate homogeneous strata for better selection in each stratum. Concentrating on a limited sampling ratio scenario, we next present an optimized sample size allocation scheme by considering the diversity of stratum's variability, with the promise of further variance reduction. Theoretically, we elaborate the explicit relation among different selection schemes with regard to variance, under heterogeneous settings, we demonstrate the effectiveness of our selection scheme. Experimental results confirm that our approach not only allows for better performance relative to state-of-the-art methods but also is compatible with prevalent FL algorithms.  ( 2 min )

We provide a new bi-criteria $\tilde{O}(\log^2 k)$ competitive algorithm for explainable $k$-means clustering. Explainable $k$-means was recently introduced by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). It is described by an easy to interpret and understand (threshold) decision tree or diagram. The cost of the explainable $k$-means clustering equals to the sum of costs of its clusters; and the cost of each cluster equals the sum of squared distances from the points in the cluster to the center of that cluster. The best non bi-criteria algorithm for explainable clustering $\tilde{O}(k)$ competitive, and this bound is tight. Our randomized bi-criteria algorithm constructs a threshold decision tree that partitions the data set into $(1+\delta)k$ clusters (where $\delta\in (0,1)$ is a parameter of the algorithm). The cost of this clustering is at most $\tilde{O}(1/ \delta \cdot \log^2 k)$ times the cost of the optimal unconstrained $k$-means clustering. We show that this bound is almost optimal.  ( 2 min )

Currently the most popular method of providing robustness certificates is randomized smoothing where an input is smoothed via some probability distribution. We propose a novel approach to randomized smoothing over multiplicative parameters. Using this method we construct certifiably robust classifiers with respect to a gamma correction perturbation and compare the result with classifiers obtained via other smoothing distributions (Gaussian, Laplace, uniform). The experiments show that asymmetrical Rayleigh distribution allows to obtain better certificates for some values of perturbation parameters. To the best of our knowledge it is the first work concerning certified robustness against the multiplicative gamma correction transformation and the first to study effects of asymmetrical distributions in randomized smoothing.  ( 2 min )

Established approaches to obtain generalization bounds in data-driven optimization and machine learning mostly build on solutions from empirical risk minimization (ERM), which depend crucially on the functional complexity of the hypothesis class. In this paper, we present an alternate route to obtain these bounds on the solution from distributionally robust optimization (DRO), a recent data-driven optimization framework based on worst-case analysis and the notion of ambiguity set to capture statistical uncertainty. In contrast to the hypothesis class complexity in ERM, our DRO bounds depend on the ambiguity set geometry and its compatibility with the true loss function. Notably, when using maximum mean discrepancy as a DRO distance metric, our analysis implies generalization bounds that depend solely on the true loss function. To the best of our knowledge, it is the first generalization bound in the literature that is entirely independent of any other candidates in the hypothesis class. We hope our findings can open the door for a better understanding of DRO, especially its benefits on loss minimization and other machine learning applications.  ( 2 min )

Practical use of neural networks often involves requirements on latency, energy and memory among others. A popular approach to find networks under such requirements is through constrained Neural Architecture Search (NAS). However, previous methods use complicated predictors for the accuracy of the network. Those predictors are hard to interpret and sensitive to many hyperparameters to be tuned, hence, the resulting accuracy of the generated models is often harmed. In this work we resolve this by introducing Bilinear Interpretable Neural Architecture Search (BINAS), that is based on an accurate and simple bilinear formulation of both an accuracy estimator and the expected resource requirement, together with a scalable search method with theoretical guarantees. The simplicity of our proposed estimator together with the intuitive way it is constructed bring interpretability through many insights about the contribution of different design choices. For example, we find that in the examined search space, adding depth and width is more effective at deeper stages of the network and at the beginning of each resolution stage. Our experiments show that BINAS generates comparable to or better architectures than other state-of-the-art NAS methods within a reduced marginal search cost, while strictly satisfying the resource constraints.  ( 2 min )

This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. The challenges of this problem are two folds: 1) Data samples ceaselessly flowing in may carry shifted patterns over time, requiring learners to update hence adapt on-the-fly. 2) Newly emerging features are described by very few samples, resulting in weak learners that tend to make error predictions. A plausible idea to overcome the challenges is to establish relationship between the pre-and-post evolving feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional media streams with complex feature interplay, which suffers an tradeoff between onlineness (biasing shallow learners) and expressiveness(requiring deep learners). Motivated by this, we propose a novel OLD^3S paradigm, where a shared latent subspace is discovered to summarize information from the old and new feature spaces, building intermediate feature mapping relationship. A key trait of OLD^3S is to treat the model capacity as a learnable semantics, yields optimal model depth and parameters jointly, in accordance with the complexity and non-linearity of the input data streams in an online fashion. Both theoretical analyses and empirical studies substantiate the viability and effectiveness of our proposal.  ( 2 min )

Computational protein design has the potential to deliver novel molecular structures, binders, and catalysts for myriad applications. Recent neural graph-based models that use backbone coordinate-derived features show exceptional performance on native sequence recovery tasks and are promising frameworks for design. A statistical framework for modeling protein sequence landscapes using Tertiary Motifs (TERMs), compact units of recurring structure in proteins, has also demonstrated good performance on protein design tasks. In this work, we investigate the use of TERM-derived data as features in neural protein design frameworks. Our graph-based architecture, TERMinator, incorporates TERM-based and coordinate-based information and outputs a Potts model over sequence space. TERMinator outperforms state-of-the-art models on native sequence recovery tasks, suggesting that utilizing TERM-based and coordinate-based features together is beneficial for protein design.  ( 2 min )

As use of data driven technologies spreads, software engineers are more often faced with the task of solving a business problem using data-driven methods such as machine learning (ML) algorithms. Deployment of ML within large software systems brings new challenges that are not addressed by standard engineering practices and as a result businesses observe high rate of ML deployment project failures. Data Oriented Architecture (DOA) is an emerging approach that can support data scientists and software developers when addressing such challenges. However, there is a lack of clarity about how DOA systems should be implemented in practice. This paper proposes to consider Flow-Based Programming (FBP) as a paradigm for creating DOA applications. We empirically evaluate FBP in the context of ML deployment on four applications that represent typical data science projects. We use Service Oriented Architecture (SOA) as a baseline for comparison. Evaluation is done with respect to different application domains, ML deployment stages, and code quality metrics. Results reveal that FBP is a suitable paradigm for data collection and data science tasks, and is able to simplify data collection and discovery when compared with SOA. We discuss the advantages of FBP as well as the gaps that need to be addressed to increase FBP adoption as a standard design paradigm for DOA.  ( 2 min )

Neural Architecture Search (NAS) has enabled automatic discovery of more efficient neural network architectures, especially for mobile and embedded vision applications. Although recent research has proposed ways of quickly estimating latency on unseen hardware devices with just a few samples, little focus has been given to the challenges of estimating latency on runtimes using optimized graphs, such as TensorRT and specifically for edge devices. In this work, we propose MAPLE-Edge, an edge device-oriented extension of MAPLE, the state-of-the-art latency predictor for general purpose hardware, where we train a regression network on architecture-latency pairs in conjunction with a hardware-runtime descriptor to effectively estimate latency on a diverse pool of edge devices. Compared to MAPLE, MAPLE-Edge can describe the runtime and target device platform using a much smaller set of CPU performance counters that are widely available on all Linux kernels, while still achieving up to +49.6% accuracy gains against previous state-of-the-art baseline methods on optimized edge device runtimes, using just 10 measurements from an unseen target device. We also demonstrate that unlike MAPLE which performs best when trained on a pool of devices sharing a common runtime, MAPLE-Edge can effectively generalize across runtimes by applying a trick of normalizing performance counters by the operator latency, in the measured hardware-runtime descriptor. Lastly, we show that for runtimes exhibiting lower than desired accuracy, performance can be boosted by collecting additional samples from the target device, with an extra 90 samples translating to gains of nearly +40%.  ( 2 min )

The usability of vehicles is highly dependent on their energy consumption. In particular, one of the main factors hindering the mass adoption of electric (EV), hybrid (HEV), and plug-in hybrid (PHEV) vehicles is range anxiety, which occurs when a driver is uncertain about the availability of energy for a given trip. To tackle this problem, we propose a machine learning approach for modeling the battery energy consumption. By reducing predictive uncertainty, this method can help increase trust in the vehicle's performance and thus boost its usability. Most related work focuses on physical and/or chemical models of the battery that affect the energy consumption. We propose a data-driven approach which relies on real-world datasets including battery related attributes. Our approach showed an improvement in terms of predictive uncertainty as well as in accuracy compared to traditional methods.  ( 2 min )

Neural text matching models have been used in a range of applications such as question answering and natural language inference, and have yielded a good performance. However, these neural models are of a limited adaptability, resulting in a decline in performance when encountering test examples from a different dataset or even a different task. The adaptability is particularly important in the few-shot setting: in many cases, there is only a limited amount of labeled data available for a target dataset or task, while we may have access to a richly labeled source dataset or task. However, adapting a model trained on the abundant source data to a few-shot target dataset or task is challenging. To tackle this challenge, we propose a Meta-Weight Regulator (MWR), which is a meta-learning approach that learns to assign weights to the source examples based on their relevance to the target loss. Specifically, MWR first trains the model on the uniformly weighted source examples, and measures the efficacy of the model on the target examples via a loss function. By iteratively performing a (meta) gradient descent, high-order gradients are propagated to the source examples. These gradients are then used to update the weights of source examples, in a way that is relevant to the target performance. As MWR is model-agnostic, it can be applied to any backbone neural model. Extensive experiments are conducted with various backbone text matching models, on four widely used datasets and two tasks. The results demonstrate that our proposed approach significantly outperforms a number of existing adaptation methods and effectively improves the cross-dataset and cross-task adaptability of the neural text matching models in the few-shot setting.  ( 2 min )

Topological Data Analysis (TDA) gives practioners the ability to analyse the global structure of cybersecurity data. We use TDA for anomaly detection in host-based logs collected with the open-source Logging Made Easy (LME) project. We present an approach that builds a filtration of simplicial complexes directly from Windows logs, enabling analysis of their intrinsic structure using topological tools. We compare the efficacy of persistent homology and the spectrum of graph and hypergraph Laplacians as feature vectors against a standard log embedding that counts events, and find that topological and spectral embeddings of computer logs contain discriminative information for classifying anomalous logs that is complementary to standard embeddings. We end by discussing the potential for our methods to be used as part of an explainable framework for anomaly detection.  ( 2 min )

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to graph-related problems such as quantum chemistry, drug discovery, and high energy physics. However, meeting demand for novel GNN models and fast inference simultaneously is challenging because of the gap between developing efficient accelerators and the rapid creation of new GNN models. Prior art focuses on the acceleration of specific classes of GNNs, such as Graph Convolutional Network (GCN), but lacks the generality to support a wide range of existing or new GNN models. Meanwhile, most work rely on graph pre-processing to exploit data locality, making them unsuitable for real-time applications. To address these limitations, in this work, we propose a generic dataflow architecture for GNN acceleration, named FlowGNN, which can flexibly support the majority of message-passing GNNs. The contributions are three-fold. First, we propose a novel and scalable dataflow architecture, which flexibly supports a wide range of GNN models with message-passing mechanism. The architecture features a configurable dataflow optimized for simultaneous computation of node embedding, edge embedding, and message passing, which is generally applicable to all models. We also propose a rich library of model-specific components. Second, we deliver ultra-fast real-time GNN inference without any graph pre-processing, making it agnostic to dynamically changing graph structures. Third, we verify our architecture on the Xilinx Alveo U50 FPGA board and measure the on-board end-to-end performance. We achieve a speed-up of up to 51-254x against CPU (6226R) and 1.3-477x against GPU (A6000) (with batch sizes 1 through 1024); we also outperform the SOTA GNN accelerator I-GCN by 1.03x and 1.25x across two datasets. Our implementation code and on-board measurement are publicly available on GitHub.  ( 2 min )

Activation functions (AF) are necessary components of neural networks that allow approximation of functions, but AFs in current use are usually simple monotonically increasing functions. In this paper, we propose trainable compound AF (TCA) composed of a sum of shifted and scaled simple AFs. TCAs increase the effectiveness of networks with fewer parameters compared to added layers. TCAs have a special interpretation in generative networks because they effectively estimate the marginal distributions of each dimension of the data using a mixture distribution, reducing modality and making linear dimension reduction more effective. When used in restricted Boltzmann machines (RBMs), they result in a novel type of RBM with mixture-based stochastic units. Improved performance is demonstrated in experiments using RBMs, deep belief networks (DBN), projected belief networks (PBN), and variational auto-encoders (VAE).  ( 2 min )

Differentially private (DP) synthetic data generation is a practical method for improving access to data as a means to encourage productive partnerships. One issue inherent to DP is that the "privacy budget" is generally "spent" evenly across features in the data set. This leads to good statistical parity with the real data, but can undervalue the conditional probabilities and marginals that are critical for predictive quality of synthetic data. Further, loss of predictive quality may be non-uniform across the data set, with subsets that correspond to minority groups potentially suffering a higher loss. In this paper, we develop ensemble methods that distribute the privacy budget "wisely" to maximize predictive accuracy of models trained on DP data, and "fairly" to bound potential disparities in accuracy across groups and reduce inequality. Our methods are based on the insights that feature importance can inform how privacy budget is allocated, and, further, that per-group feature importance and fairness-related performance objectives can be incorporated in the allocation. These insights make our methods tunable to social contexts, allowing data owners to produce balanced synthetic data for predictive analysis.  ( 2 min )

To act safely and ethically in the real world, agents must be able to reason about harm and avoid harmful actions. In this paper we develop the first statistical definition of harm and a framework for factoring harm into algorithmic decisions. We argue that harm is fundamentally a counterfactual quantity, and show that standard machine learning algorithms are guaranteed to pursue harmful policies in certain environments. To resolve this, we derive a family of counterfactual objective functions that robustly mitigate for harm. We demonstrate our approach with a statistical model for identifying optimal drug doses. While identifying optimal doses using the causal treatment effect results in harmful treatment decisions, our counterfactual algorithm identifies doses that are far less harmful without sacrificing efficacy. Our results show that counterfactual reasoning is a key ingredient for safe and ethical AI.  ( 2 min )

Head pose estimation is a crucial challenge for many real-world applications, such as attention and human behavior analysis. This paper aims to estimate head pose from a single image by applying notions of network curvature. In the real world, many complex networks have groups of nodes that are well connected to each other with significant functional roles. Similarly, the interactions of facial landmarks can be represented as complex dynamic systems modeled by weighted graphs. The functionalities of such systems are therefore intrinsically linked to the topology and geometry of the underlying graph. In this work, using the geometric notion of Ollivier-Ricci curvature (ORC) on weighted graphs as input to the XGBoost regression model, we show that the intrinsic geometric basis of ORC offers a natural approach to discovering underlying common structure within a pool of poses. Experiments on the BIWI, AFLW2000 and Pointing'04 datasets show that the ORC_XGB method performs well compared to state-of-the-art methods, both landmark-based and image-only.

Building on (Neal and Hinton, 1998), where the problem tackled by EM is recast as the optimization of a free energy functional on an infinite-dimensional space, we obtain three practical particle-based alternatives to EM applicable to broad classes of models. All three are derived through straightforward discretizations of gradient flows associated with the functional. The novel algorithms scale well to high-dimensional settings and outperform existing state-of-the-art methods in numerical experiments.

Epicardial adipose tissue is a type of adipose tissue located between the heart wall and a protective layer around the heart called the pericardium. The volume and thickness of epicardial adipose tissue are linked to various cardiovascular diseases. It is shown to be an independent cardiovascular disease risk factor. Fully automatic and reliable measurements of epicardial adipose tissue from CT scans could provide better disease risk assessment and enable the processing of large CT image data sets for a systemic epicardial adipose tissue study. This paper proposes a method for fully automatic semantic segmentation of epicardial adipose tissue from CT images using a deep neural network. The proposed network uses a U-Net-based architecture with slice depth information embedded in the input image to segment a pericardium region of interest, which is used to obtain an epicardial adipose tissue segmentation. Image augmentation is used to increase model robustness. Cross-validation of the proposed method yields a Dice score of 0.86 on the CT scans of 20 patients.  ( 2 min )

Statistical Inference is the process of determining a probability distribution over the space of parameters of a model given a data set. As more data becomes available this probability distribution becomes updated via the application of Bayes' theorem. We present a treatment of this Bayesian updating process as a continuous dynamical system. Statistical inference is then governed by a first order differential equation describing a trajectory or flow in the information geometry determined by a parametric family of models. We solve this equation for some simple models and show that when the Cram\'{e}r-Rao bound is saturated the learning rate is governed by a simple $1/T$ power-law, with $T$ a time-like variable denoting the quantity of data. The presence of hidden variables can be incorporated in this setting, leading to an additional driving term in the resulting flow equation. We illustrate this with both analytic and numerical examples based on Gaussians and Gaussian Random Processes and inference of the coupling constant in the 1D Ising model. Finally we compare the qualitative behaviour exhibited by Bayesian flows to the training of various neural networks on benchmarked data sets such as MNIST and CIFAR10 and show how that for networks exhibiting small final losses the simple power-law is also satisfied.  ( 2 min )

Many deep learning applications, like keyword spotting, require the incorporation of new concepts (classes) over time, referred to as Class Incremental Learning (CIL). The major challenge in CIL is catastrophic forgetting, i.e., preserving as much of the old knowledge as possible while learning new tasks. Various techniques, such as regularization, knowledge distillation, and the use of exemplars, have been proposed to resolve this issue. However, prior works primarily focus on the incremental learning step, while ignoring the optimization during the base model training. We hypothesize that a more transferable and generalizable feature representation from the base model would be beneficial to incremental learning. In this work, we adopt multitask learning during base model training to improve the feature generalizability. Specifically, instead of training a single model with all the base classes, we decompose the base classes into multiple subsets and regard each of them as a task. These tasks are trained concurrently and a shared feature extractor is obtained for incremental learning. We evaluate our approach on two datasets under various configurations. The results show that our approach enhances the average incremental learning accuracy by up to 5.5%, which enables more reliable and accurate keyword spotting over time. Moreover, the proposed approach can be combined with many existing techniques and provides additional performance gain.  ( 2 min )

The article is concerned with the problem of multi-step financial time series forecasting of Foreign Exchange (FX) rates. To address this problem, we introduce a parameter-free regression network termed RegPred Net. The exchange rate to forecast is treated as a stochastic process. It is assumed to follow a generalization of Brownian motion and the mean-reverting process referred to as the generalized Ornstein-Uhlenbeck (OU) process, with time-dependent coefficients. Using past observed values of the input time series, these coefficients can be regressed online by the cells of the first half of the network (Reg). The regressed coefficients depend only on - but are very sensitive to - a small number of hyperparameters required to be set by a global optimization procedure for which, Bayesian optimization is an adequate heuristic. Thanks to its multi-layered architecture, the second half of the regression network (Pred) can project time-dependent values for the OU process coefficients and generate realistic trajectories of the time series. Predictions can be easily derived in the form of expected values estimated by averaging values obtained by Monte Carlo simulation. The forecasting accuracy on a 100 days horizon is evaluated for several of the most important FX rates such as EUR/USD, EUR/CNY, and EUR/GBP. Our experimental results show that the RegPred Net significantly outperforms ARMA, ARIMA, LSTMs, and Autoencoder-LSTM models in this task.  ( 2 min )

Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly consist of two stages, i.e., selecting the top-ranked nodes and removing the rest nodes to construct a coarsened graph representation. However, local structural information of the removed nodes would be inevitably dropped in these methods, due to the inherent coupling of nodes (location) and their features (signals). In this paper, we propose an enhanced three-stage method via lifting, named LiftPool, to improve hierarchical graph representation by maximally preserving the local structural information in graph pooling. LiftPool introduces an additional stage of graph lifting before graph coarsening to preserve the local information of the removed nodes and decouple the processes of node removing and feature reduction. Specifically, for each node to be removed, its local information is obtained by subtracting the global information aggregated from its neighboring preserved nodes. Subsequently, this local information is aligned and propagated to the preserved nodes to alleviate information loss in graph coarsening. Furthermore, we demonstrate that the proposed LiftPool is localized and permutation-invariant. The proposed graph lifting structure is general to be integrated with existing downsampling-based graph pooling methods. Evaluations on benchmark graph datasets show that LiftPool substantially outperforms the state-of-the-art graph pooling methods in the task of graph classification.  ( 2 min )

This paper compares the performances of three supervised machine learning algorithms in terms of predictive ability and model interpretation on structured or tabular data. The algorithms considered were scikit-learn implementations of extreme gradient boosting machines (XGB) and random forests (RFs), and feedforward neural networks (FFNNs) from TensorFlow. The paper is organized in a findings-based manner, with each section providing general conclusions supported by empirical results from simulation studies that cover a wide range of model complexity and correlation structures among predictors. We considered both continuous and binary responses of different sample sizes. Overall, XGB and FFNNs were competitive, with FFNNs showing better performance in smooth models and tree-based boosting algorithms performing better in non-smooth models. This conclusion held generally for predictive performance, identification of important variables, and determining correct input-output relationships as measured by partial dependence plots (PDPs). FFNNs generally had less over-fitting, as measured by the difference in performance between training and testing datasets. However, the difference with XGB was often small. RFs did not perform well in general, confirming the findings in the literature. All models exhibited different degrees of bias seen in PDPs, but the bias was especially problematic for RFs. The extent of the biases varied with correlation among predictors, response type, and data set sample size. In general, tree-based models tended to over-regularize the fitted model in the tails of predictor distributions. Finally, as to be expected, performances were better for continuous responses compared to binary data and with larger samples.  ( 2 min )

Predicitions made by neural networks can be fraudulently altered by so-called poisoning attacks. A special case are backdoor poisoning attacks. We study suitable detection methods and introduce a new method called Heatmap Clustering. There, we apply a $k$-means clustering algorithm on heatmaps produced by the state-of-the-art explainable AI method Layer-wise relevance propagation. The goal is to separate poisoned from un-poisoned data in the dataset. We compare this method with a similar method, called Activation Clustering, which also uses $k$-means clustering but applies it on the activation of certain hidden layers of the neural network as input. We test the performance of both approaches for standard backdoor poisoning attacks, label-consistent poisoning attacks and label-consistent poisoning attacks with reduced amplitude stickers. We show that Heatmap Clustering consistently performs better than Activation Clustering. However, when considering label-consistent poisoning attacks, the latter method also yields good detection performance.  ( 2 min )

The Reinforcement Learning (RL) paradigm has been an essential tool for automating robotic tasks. Despite the advances in RL, it is still not widely adopted in the industry due to the need for an expensive large amount of robot interaction with its environment. Curriculum Learning (CL) has been proposed to expedite learning. However, most research works have been only evaluated in simulated environments, from video games to robotic toy tasks. This paper presents a study for accelerating robot learning of contact-rich manipulation tasks based on Curriculum Learning combined with Domain Randomization (DR). We tackle complex industrial assembly tasks with position-controlled robots, such as insertion tasks. We compare different curricula designs and sampling approaches for DR. Based on this study, we propose a method that significantly outperforms previous work, which uses DR only (No CL is used), with less than a fifth of the training time (samples). Results also show that even when training only in simulation with toy tasks, our method can learn policies that can be transferred to the real-world robot. The learned policies achieved success rates of up to 86\% on real-world complex industrial insertion tasks (with tolerances of $\pm 0.01~mm$) not seen during the training.  ( 2 min )

Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods generally assume at least one of (i) source and target task label spaces must overlap, (ii) source datasets are available, and (iii) target network architectures are consistent with source ones. However, these all assumptions are difficult to hold in practical settings because the target task rarely has the same labels as the source task, the source dataset access is restricted due to licensing and storage costs, and the target architecture is often specialized to each task. To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a target architecture with a synthesized dataset by using conditional source generative models. P-SSL applies SSL algorithms to labeled target data and unlabeled pseudo samples, which are generated by cascading the source classifier and generative models to condition them with target samples. Our experimental results indicate that our method can outperform baselines of scratch training and knowledge distillation.  ( 2 min )

In past years, the world has switched to many-core and multi-core shared memory architectures. As a result, there is a growing need to utilize these architectures by introducing shared memory parallelization schemes to software applications. OpenMP is the most comprehensive API that implements such schemes, characterized by a readable interface. Nevertheless, introducing OpenMP into code is challenging due to pervasive pitfalls in management of parallel shared memory. To facilitate the performance of this task, many source-to-source (S2S) compilers have been created over the years, tasked with inserting OpenMP directives into code automatically. In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable code and generating appropriate directives. In this work, we propose leveraging recent advances in ML techniques, specifically in natural language processing (NLP), to replace S2S compilers altogether. We create a database (corpus), Open-OMP, specifically for this goal. Open-OMP contains over 28,000 code snippets, half of which contain OpenMP directives while the other half do not need parallelization at all with high probability. We use the corpus to train systems to automatically classify code segments in need of parallelization, as well as suggest individual OpenMP clauses. We train several transformer models, named PragFormer, for these tasks, and show that they outperform statistically-trained baselines and automatic S2S parallelization compilers in both classifying the overall need for an OpenMP directive and the introduction of private and reduction clauses. Our source code and database are available at: https://github.com/Scientific-Computing-Lab-NRCN/PragFormer.  ( 2 min )

Similarity metric is crucial for massive MIMO positioning utilizing channel state information~(CSI). In this letter, we propose a novel massive MIMO CSI similarity learning method via deep convolutional neural network~(DCNN) and contrastive learning. A contrastive loss function is designed considering multiple positive and negative CSI samples drawn from a training dataset. The DCNN encoder is trained using the loss so that positive samples are mapped to points close to the anchor's encoding, while encodings of negative samples are kept away from the anchor's in the representation space. Evaluation results of fingerprint-based positioning on a real-world CSI dataset show that the learned similarity metric improves positioning accuracy significantly compared with other known state-of-the-art methods.  ( 2 min )

We propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme that originates from the tree representation of graphs. In the tree representation, messages propagate upward from the leaf nodes to the root node, and each node preserves its initial information prior to receiving information from its child nodes (neighbors). We formulate a general propagation rule following the nature of message passing in the tree to update a node's feature by aggregating its initial feature and its neighbor nodes' updated features. Two graph representation learning models are proposed within this GTNet architecture - Graph Tree Attention Network (GTAN) and Graph Tree Convolution Network (GTCN), with experimentally demonstrated state-of-the-art performance on several popular benchmark datasets. Unlike the vanilla Graph Attention Network (GAT) and Graph Convolution Network (GCN) which have the "over-smoothing" issue, the proposed GTAN and GTCN models can go deep as demonstrated by comprehensive experiments and rigorous theoretical analysis.  ( 2 min )

Scientific publications about machine learning in healthcare are often about implementing novel methods and boosting the performance - at least from a computer science perspective. However, beyond such often short-lived improvements, much more needs to be taken into consideration if we want to arrive at a sustainable progress in healthcare. What does it take to actually implement such a system, make it usable for the domain expert, and possibly bring it into practical usage? Targeted at Computer Scientists, this work presents a multidisciplinary view on machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. Along with an implemented risk prediction system in nephrology, challenges and lessons learned in a pilot project are presented.  ( 2 min )

Given input-output pairs from a parabolic partial differential equation (PDE) in any spatial dimension $n\geq 1$, we derive the first theoretically rigorous scheme for learning the associated Green's function $G$. Until now, rigorously learning Green's functions associated with parabolic operators has been a major challenge in the field of scientific machine learning because $G$ may not be square-integrable when $n>1$, and time-dependent PDEs have transient dynamics. By combining the hierarchical low-rank structure of $G$ together with the randomized singular value decomposition, we construct an approximant to $G$ that achieves a relative error of $\smash{\mathcal{O}(\Gamma_\epsilon^{-1/2}\epsilon)}$ in the $L^1$-norm with high probability by using at most $\smash{\mathcal{O}(\epsilon^{-\frac{n+2}{2}}\log(1/\epsilon))}$ input-output training pairs, where $\Gamma_\epsilon$ is a measure of the quality of the training dataset for learning $G$, and $\epsilon>0$ is sufficiently small. Along the way, we extend the low-rank theory of Bebendorf and Hackbusch from elliptic PDEs in dimension $1\leq n\leq 3$ to parabolic PDEs in any dimensions, which shows that Green's functions associated with parabolic PDEs admit a low-rank structure on well-separated domains.  ( 2 min )

Deep operator networks (DeepONets) are powerful architectures for fast and accurate emulation of complex dynamics. As their remarkable generalization capabilities are primarily enabled by their projection-based attribute, we investigate connections with low-rank techniques derived from the singular value decomposition (SVD). We demonstrate that some of the concepts behind proper orthogonal decomposition (POD)-neural networks can improve DeepONet's design and training phases. These ideas lead us to a methodology extension that we name SVD-DeepONet. Moreover, through multiple SVD analyses, we find that DeepONet inherits from its projection-based attribute strong inefficiencies in representing dynamics characterized by symmetries. Inspired by the work on shifted-POD, we develop flexDeepONet, an architecture enhancement that relies on a pre-transformation network for generating a moving reference frame and isolating the rigid components of the dynamics. In this way, the physics can be represented on a latent space free from rotations, translations, and stretches, and an accurate projection can be performed to a low-dimensional basis. In addition to flexibility and interpretability, the proposed perspectives increase DeepONet's generalization capabilities and computational efficiencies. For instance, we show flexDeepONet can accurately surrogate the dynamics of 19 variables in a combustion chemistry application by relying on 95% less trainable parameters than the ones of the vanilla architecture. We argue that DeepONet and SVD-based methods can reciprocally benefit from each other. In particular, the flexibility of the former in leveraging multiple data sources and multifidelity knowledge in the form of both unstructured data and physics-informed constraints has the potential to greatly extend the applicability of methodologies such as POD and PCA.  ( 2 min )

We introduce a new closed-loop architecture for the online solution of approximate optimal control problems in the context of continuous-time systems. Specifically, we introduce the first algorithm that incorporates dynamic momentum in actor-critic structures to control continuous-time dynamic plants with an affine structure in the input. By incorporating dynamic momentum in our algorithm, we are able to accelerate the convergence properties of the closed-loop system, achieving superior transient performance compared to traditional gradient-descent based techniques. In addition, by leveraging the existence of past recorded data with sufficiently rich information properties, we dispense with the persistence of excitation condition traditionally imposed on the regressors of the critic and the actor. Given that our continuous-time momentum-based dynamics also incorporate periodic discrete-time resets that emulate restarting techniques used in the machine learning literature, we leverage tools from hybrid dynamical systems theory to establish asymptotic stability properties for the closed-loop system. We illustrate our results with a numerical example.  ( 2 min )

Recently, much progress has been made for self-supervised action recognition. Most existing approaches emphasize the contrastive relations among videos, including appearance and motion consistency. However, two main issues remain for existing pre-training methods: 1) the learned representation is neutral and not informative for a specific task; 2) multi-task learning-based pre-training sometimes leads to sub-optimal solutions due to inconsistent domains of different tasks. To address the above issues, we propose a novel action recognition pre-training framework, which exploits human-centered prior knowledge that generates more informative representation, and avoids the conflict between multiple tasks by using task-dependent representations. Specifically, we distill knowledge from a human parsing model to enrich the semantic capability of representation. In addition, we combine knowledge distillation with contrastive learning to constitute a task-dependent multi-task framework. We achieve state-of-the-art performance on two popular benchmarks for action recognition task, i.e., UCF101 and HMDB51, verifying the effectiveness of our method.  ( 2 min )

We study a family of adversarial multiclass classification problems and provide equivalent reformulations in terms of: 1) a family of generalized barycenter problems introduced in the paper and 2) a family of multimarginal optimal transport problems where the number of marginals is equal to the number of classes in the original classification problem. These new theoretical results reveal a rich geometric structure of adversarial learning problems in multiclass classification and extend recent results restricted to the binary classification setting. A direct computational implication of our results is that by solving either the barycenter problem and its dual, or the MOT problem and its dual, we can recover the optimal robust classification rule and the optimal adversarial strategy for the original adversarial problem. Examples with synthetic and real data illustrate our results.  ( 2 min )

Recently, convolutional neural networks (CNNs) and attention mechanisms have been widely used in image denoising and achieved satisfactory performance. However, the previous works mostly use a single head to receive the noisy image, limiting the richness of extracted features. Therefore, a novel CNN with multiple heads (MH) named MHCNN is proposed in this paper, whose heads will receive the input images rotated by different rotation angles. MH makes MHCNN simultaneously utilize features of rotated images to remove noise. We also present a novel multi-path attention mechanism (MPA) to integrate these features effectively. Unlike previous attention mechanisms that handle pixel-level, channel-level, and patch-level features, MPA focuses on features at the image level. Experiments show MHCNN surpasses other state-of-the-art CNN models on additive white Gaussian noise (AWGN) denoising and real-world image denoising. Its peak signal-to-noise ratio (PSNR) results are higher than other networks, such as DnCNN, BRDNet, RIDNet, PAN-Net, and CSANN. It is also demonstrated that the proposed MH with MPA mechanism can be used as a pluggable component.  ( 2 min )

This paper presents a personalized character recommendation system for Multiplayer Online Battle Arena (MOBA) games which are considered as one of the most popular online video game genres around the world. When playing MOBA games, players go through a draft stage, where they alternately select a virtual character to play. When drafting, players select characters by not only considering their character preferences, but also the synergy and competence of their team's character combination. However, the complexity of drafting induces difficulties for beginners to choose the appropriate characters based on the characters of their team while considering their own champion preferences. To alleviate this problem, we propose DraftRec, a novel hierarchical model which recommends characters by considering each player's champion preferences and the interaction between the players. DraftRec consists of two networks: the player network and the match network. The player network captures the individual player's champion preference, and the match network integrates the complex relationship between the players and their respective champions. We train and evaluate our model from a manually collected 280,000 matches of League of Legends and a publicly available 50,000 matches of Dota2. Empirically, our method achieved state-of-the-art performance in character recommendation and match outcome prediction task. Furthermore, a comprehensive user survey confirms that DraftRec provides convincing and satisfying recommendations. Our code and dataset are available at https://github.com/dojeon-ai/DraftRec.  ( 2 min )

Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server. Most existing FL algorithms require models of identical architecture to be deployed across the clients and server, making it infeasible to train large models due to clients' limited system resources. In this work, we propose a novel ensemble knowledge transfer method named Fed-ET in which small models (different in architecture) are trained on clients, and used to train a larger model at the server. Unlike in conventional ensemble learning, in FL the ensemble can be trained on clients' highly heterogeneous data. Cognizant of this property, Fed-ET uses a weighted consensus distillation scheme with diversity regularization that efficiently extracts reliable consensus from the ensemble while improving generalization by exploiting the diversity within the ensemble. We show the generalization bound for the ensemble of weighted models trained on heterogeneous datasets that supports the intuition of Fed-ET. Our experiments on image and language tasks show that Fed-ET significantly outperforms other state-of-the-art FL algorithms with fewer communicated parameters, and is also robust against high data-heterogeneity.  ( 2 min )

This paper studies the gap between the classical pricing theory and the data-based pricing theory. We focus on the problem of price discrimination with a continuum of buyer types based on a finite sample of observations. Our first set of results provides sharp lower bounds in the worst-case scenario for the discrepancy between any data-based pricing strategies and the theoretical optimal third-degree price discrimination (3PD) strategy (respectively, uniform pricing strategy) derived from the distribution (where the sample is drawn) ranging over a large class of distributions. Consequently, there is an inevitable gap between revenues based on any data-based pricing strategy and the revenue based on the theoretical optimal 3PD (respectively, uniform pricing) strategy. We then propose easy-to-implement data-based 3PD and uniform pricing strategies and show each strategy is minimax optimal in the sense that the gap between their respective revenue and the revenue based on the theoretical optimal 3PD (respectively, uniform pricing) strategy matches our worst-case lower bounds up to constant factors (that are independent of the sample size $n$). We show that 3PD strategies are revenue superior to uniform pricing strategies if and only if the sample size $n$ is large enough. In other words, if $n$ is below a threshold, uniform pricing strategies are revenue superior to 3PD strategies. We further provide upper bounds for the gaps between the welfare generated by our minimax optimal 3PD (respectively, uniform pricing) strategy and the welfare based on the theoretical optimal 3PD (respectively, uniform pricing) strategy.  ( 2 min )

Reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons. This paper presents a new approach that utilizes relational abstractions in conjunction with deep learning to learn a generalizable Q-function for such problems. The learned Q-function can be efficiently transferred to related problems that have different object names and object quantities, and thus, entirely different state spaces. We show that the learned generalized Q-function can be utilized for zero-shot transfer to related problems without an explicit, hand-coded curriculum. Empirical evaluations on a range of problems show that our method facilitates efficient zero-shot transfer of learned knowledge to much larger problem instances containing many objects.  ( 2 min )

We provide a unifying framework where artificial neural networks and their architectures can be formally described as particular cases of a general mathematical construction--machines of finite depth. Unlike neural networks, machines have a precise definition, from which several properties follow naturally. Machines of finite depth are modular (they can be combined), efficiently computable and differentiable. The backward pass of a machine is again a machine and can be computed without overhead using the same procedure as the forward pass. We prove this statement theoretically and practically, via a unified implementation that generalizes several classical architectures--dense, convolutional, and recurrent neural networks with a rich shortcut structure--and their respective backpropagation rules.  ( 2 min )

Distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control. Significant research efforts have been devoted to developing and analyzing new algorithms for various applications. In this work, we provide a fresh perspective to understand, analyze, and design distributed optimization algorithms. Through the lens of multi-rate feedback control, we show that a wide class of distributed algorithms, including popular decentralized/federated schemes, can be viewed as discretizing a certain continuous-time feedback control system, possibly with multiple sampling rates, such as decentralized gradient descent, gradient tracking, and federated averaging. This key observation not only allows us to develop a generic framework to analyze the convergence of the entire algorithm class. More importantly, it also leads to an interesting way of designing new distributed algorithms. We develop the theory behind our framework and provide examples to highlight how the framework can be used in practice.  ( 2 min )

This paper discusses a special kind of a simple yet possibly powerful algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning method predicting unknown nodal values in a network using known nodal values and the network structure. We aim to find out how to configure the special kind of the model in applying the algorithm. To be more specific, we focus on SKG with a Gaussian kernel and specify how to find a suitable variance for the kernel. To do so, we introduce two variables with which we are able to set up requirements on the variance of the Gaussian kernel to achieve (near-) optimal performance and can better understand how SKG works. Our contribution is that we introduce two variables as analysis tools, illustrate how predictions will be affected under different Gaussian kernels, and provide an algorithm finding a suitable Gaussian kernel for SKG with knowledge about the training network. Simulation results on real datasets are provided.  ( 2 min )

We study the adversarial bandit problem with composite anonymous delayed feedback. In this setting, losses of an action are split into $d$ components, spreading over consecutive rounds after the action is chosen. And in each round, the algorithm observes the aggregation of losses that come from the latest $d$ rounds. Previous works focus on oblivious adversarial setting, while we investigate the harder non-oblivious setting. We show non-oblivious setting incurs $\Omega(T)$ pseudo regret even when the loss sequence is bounded memory. However, we propose a wrapper algorithm which enjoys $o(T)$ policy regret on many adversarial bandit problems with the assumption that the loss sequence is bounded memory. Especially, for $K$-armed bandit and bandit convex optimization, we have $\mathcal{O}(T^{2/3})$ policy regret bound. We also prove a matching lower bound for $K$-armed bandit. Our lower bound works even when the loss sequence is oblivious but the delay is non-oblivious. It answers the open problem proposed in \cite{wang2021adaptive}, showing that non-oblivious delay is enough to incur $\tilde{\Omega}(T^{2/3})$ regret.  ( 2 min )

Graph clustering discovers groups or communities within networks. Deep learning methods such as autoencoders (AE) extract effective clustering and downstream representations but cannot incorporate rich structural information. While Graph Neural Networks (GNN) have shown great success in encoding graph structure, typical GNNs based on convolution or attention variants suffer from over-smoothing, noise, heterophily, are computationally expensive and typically require the complete graph being present. Instead, we propose Self-Supervised Contrastive Graph Clustering (SCGC), which imposes graph-structure via contrastive loss signals to learn discriminative node representations and iteratively refined soft cluster labels. We also propose SCGC*, with a more effective, novel, Influence Augmented Contrastive (IAC) loss to fuse richer structural information, and half the original model parameters. SCGC(*) is faster with simple linear units, completely eliminate convolutions and attention of traditional GNNs, yet efficiently incorporates structure. It is impervious to layer depth and robust to over-smoothing, incorrect edges and heterophily. It is scalable by batching, a limitation in many prior GNN models, and trivially parallelizable. We obtain significant improvements over state-of-the-art on a wide range of benchmark graph datasets, including images, sensor data, text, and citation networks efficiently. Specifically, 20% on ARI and 18% on NMI for DBLP; overall 55% reduction in training time and overall, 81% reduction on inference time. Our code is available at : https://github.com/gayanku/SCGC  ( 2 min )

This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The idea is to create complementary representations promoting diversity by varying the autoencoders' initialization, architecture, and training data. SVM, Bagging, Random Forest, and a dynamic ensemble selection method are evaluated as final classification methods. Experimental results on Jaffe and Cohn-Kanade datasets using a leave-one-subject-out protocol show that FER methods based on the proposed diverse representations compare favorably against state-of-the-art approaches that also explore unsupervised feature learning.  ( 2 min )

Early fault detection (EFD) of rolling bearings can recognize slight deviation of the health states and contribute to the stability of mechanical systems. In practice, very limited target bearing data are available to conduct EFD, which makes it hard to adapt to the EFD task of new bearings. To address this problem, many transfer learning based EFD methods utilize historical data to learn transferable domain knowledge and conduct early fault detection on new target bearings. However, most existing methods only consider the distribution drift across different working conditions but ignore the difference between bearings under the same working condition, which is called Unit-to-Unit Variability (UtUV). The setting of EFD with limited target data considering UtUV can be formulated as a Few-shot Anomaly Detection task. Therefore, this paper proposes a novel EFD method based on meta-learning considering UtUV. The proposed method can learn a generic metric based on Relation Network (RN) to measure the similarity between normal data and the new arrival target bearing data. Besides, the proposed method utilizes a health state embedding strategy to decrease false alarms. The performance of proposed method is tested on two bearing datasets. The results show that the proposed method can detect incipient faults earlier than the baselines with lower false alarms.  ( 2 min )

The recent rise in deep learning technologies fueled innovation and boosted scientific research. Their achievements enabled new research directions for deep generative modeling (DGM), an increasingly popular approach that can create novel and unseen data, starting from a given data set. As the technology shows promising applications, many ethical issues also arise. For example, their misuse can enable disinformation campaigns and powerful phishing attempts. Research also indicates different biases affect deep learning models, leading to social issues such as misrepresentation. In this work, we formulate a novel setting to deal with similar problems, showing that a repurposed anomaly detection system effectively generates novel data, avoiding generating specified unwanted data. We propose Variational Auto-encoding Binary Classifiers (V-ABC): a novel model that repurposes and extends the Auto-encoding Binary Classifier (ABC) anomaly detector, using the Variational Auto-encoder (VAE). We survey the limitations of existing approaches and explore many tools to show the model's inner workings in an interpretable way. This proposal has excellent potential for generative applications: models that rely on user-generated data could automatically filter out unwanted content, such as offensive language, obscene images, and misleading information.  ( 2 min )

Physics-informed Neural Networks (PINNs) are gaining attention in the engineering and scientific literature for solving a range of differential equations with applications in weather modeling, healthcare, manufacturing, and so on. Poor scalability is one of the barriers to utilizing PINNs for many real-world problems. To address this, a Self-scalable tanh (Stan) activation function is proposed for the PINNs. The proposed Stan function is smooth, non-saturating, and has a trainable parameter. During training, it can allow easy flow of gradients to compute the required derivatives and also enable systematic scaling of the input-output mapping. It is also shown theoretically that the PINN with the proposed Stan function has no spurious stationary points when using gradient descent algorithms. The proposed Stan is tested on a couple of numerical studies involving general regression problems. It is subsequently used for solving multiple forward problems, which involve second-order derivatives and multiple dimensions, and an inverse problem where the thermal diffusivity is predicted through heat conduction in a rod. Our results of these case studies establish empirically that the Stan activation function can achieve better training and more accurate predictions than the state-of-the-art activation functions.  ( 2 min )

We consider a rate-constrained contextual multi-armed bandit (RC-CMAB) problem, in which a group of agents are solving the same contextual multi-armed bandit (CMAB) problem. However, the contexts are observed by a remotely connected entity, i.e., the decision-maker, that updates the policy to maximize the returned rewards, and communicates the arms to be sampled by the agents to a controller over a rate-limited communications channel. This framework can be applied to personalized ad placement, whenever the content owner observes the website visitors, and hence has the context, but needs to transmit the ads to be shown to a controller that is in charge of placing the marketing content. Consequently, the rate-constrained CMAB (RC-CMAB) problem requires the study of lossy compression schemes for the policy to be employed whenever the constraint on the channel rate does not allow the uncompressed transmission of the decision-maker's intentions. We characterize the fundamental information theoretic limits of this problem by letting the number of agents go to infinity, and study the regret that can be achieved, identifying the two distinct rate regions leading to linear and sub-linear regrets respectively. We then analyze the optimal compression scheme achievable in the limit with infinite agents, when using the forward and reverse KL divergence as distortion metric. Based on this, we also propose a practical coding scheme, and provide numerical results.  ( 2 min )

Offline reinforcement learning (RL) aims to find near-optimal policies from logged data without further environment interaction. Model-based algorithms, which learn a model of the environment from the dataset and perform conservative policy optimisation within that model, have emerged as a promising approach to this problem. In this work, we present Robust Adversarial Model-Based Offline RL (RAMBO), a novel approach to model-based offline RL. To achieve conservatism, we formulate the problem as a two-player zero sum game against an adversarial environment model. The model is trained minimise the value function while still accurately predicting the transitions in the dataset, forcing the policy to act conservatively in areas not covered by the dataset. To approximately solve the two-player game, we alternate between optimising the policy and optimising the model adversarially. The problem formulation that we address is theoretically grounded, resulting in a PAC performance guarantee and a pessimistic value function which lower bounds the value function in the true environment. We evaluate our approach on widely studied offline RL benchmarks, and demonstrate that our approach achieves state of the art performance.  ( 2 min )

Social media platforms often act as breeding grounds for various forms of trolling or malicious content targeting users or communities. One way of trolling users is by creating memes, which in most cases unites an image with a short piece of text embedded on top of it. The situation is more complex for multilingual(e.g., Tamil) memes due to the lack of benchmark datasets and models. We explore several models to detect Troll memes in Tamil based on the shared task, "Troll Meme Classification in DravidianLangTech2022" at ACL-2022. We observe while the text-based model MURIL performs better for Non-troll meme classification, the image-based model VGG16 performs better for Troll-meme classification. Further fusing these two modalities help us achieve stable outcomes in both classes. Our fusion model achieved a 0.561 weighted average F1 score and ranked second in this task.  ( 2 min )

Theoretically, the accuracy of computational models in predicting compound-protein binding affinities (CPAs) could be improved by the introduction of protein 3D structure information. However, most of these models still suffer from a low accuracy due to the lack of an efficient approach to encode informative protein features. The major challenge is how to combine the multi-modal information such as the residue sequence of the protein, residue atom coordinates and the torsion angles. To tackle this problem, we develop Fast Evolutional Attention and Thoroughgoing-graph Neural Networks (FeatNN) to facilitate the application of protein 3D structure information for predicting CPAs. Specifically, we established a novel end-to-end architecture to jointly embed torsion matrix, discrete distance matrix, and sequence information of protein and extract compound features with deep graph convolution layers. In addition, a new pairwise mapping attention mechanism is introduced to comprehensively learn potential interaction information between proteins and compounds. FeatNN considerably outperforms various state-of-the-art baselines in CPA prediction with the Pearson value elevated by about 35.7%. Thus, FeatNN provides an outstanding method for highly accurate CPA prediction and facilitates high-throughput virtual screening of drug candidates.  ( 2 min )

Signalized intersections in arterial roads result in persistent vehicle idling and excess accelerations, contributing to fuel consumption and CO2 emissions. There has thus been a line of work studying eco-driving control strategies to reduce fuel consumption and emission levels at intersections. However, methods to devise effective control strategies across a variety of traffic settings remain elusive. In this paper, we propose a reinforcement learning (RL) approach to learn effective eco-driving control strategies. We analyze the potential impact of a learned strategy on fuel consumption, CO2 emission, and travel time and compare with naturalistic driving and model-based baselines. We further demonstrate the generalizability of the learned policies under mixed traffic scenarios. Simulation results indicate that scenarios with 100% penetration of connected autonomous vehicles (CAV) may yield as high as 18% reduction in fuel consumption and 25% reduction in CO2 emission levels while even improving travel speed by 20%. Furthermore, results indicate that even 25% CAV penetration can bring at least 50% of the total fuel and emission reduction benefits.  ( 2 min )

To avoid discriminatory uses of their data, organizations can learn to map them into a representation that filters out information related to sensitive attributes. However, all existing methods in fair representation learning generate a fairness-information trade-off. To achieve different points on the fairness-information plane, one must train different models. In this paper, we first demonstrate that fairness-information trade-offs are fully characterized by rate-distortion trade-offs. Then, we use this key result and propose SoFaiR, a single shot fair representation learning method that generates with one trained model many points on the fairness-information plane. Besides its computational saving, our single-shot approach is, to the extent of our knowledge, the first fair representation learning method that explains what information is affected by changes in the fairness / distortion properties of the representation. Empirically, we find on three datasets that SoFaiR achieves similar fairness-information trade-offs as its multi-shot counterparts.  ( 2 min )

Chemical plant design and optimisation have proven challenging due to the complexity of these real-world systems. The resulting complexity translates into high computational costs for these systems' mathematical formulations and simulation models. Research has illustrated the benefits of using machine learning surrogate models as substitutes for computationally expensive models during optimisation. This paper extends recent research into optimising chemical plant design and operation. The study further explores Surrogate Assisted Genetic Algorithms (SA-GA) in more complex variants of the original plant design and optimisation problems, such as the inclusion of parallel and feedback components. The novel extension to the original algorithm proposed in this study, Surrogate Assisted NSGA-\Romannum{2} (SA-NSGA), was tested on a popular literature case, the Pressure Swing Adsorption (PSA) system. We further provide extensive experimentation, comparing various meta-heuristic optimisation techniques and numerous machine learning models as surrogates. The results for both sets of systems illustrate the benefits of using Genetic Algorithms as an optimisation framework for complex chemical plant system design and optimisation for both single and multi-objective scenarios. We confirm that Random Forest surrogate assisted Evolutionary Algorithms can be scaled to increasingly complex chemical systems with parallel and feedback components. We further find that combining a Genetic Algorithm framework with Machine Learning Surrogate models as a substitute for long-running simulation models yields significant computational efficiency improvements, 1.7 - 1.84 times speedup for the increased complexity examples and a 2.7 times speedup for the Pressure Swing Adsorption system.  ( 2 min )

Advances in multi-agent reinforcement learning(MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving. Explaining agent decisions are crucial for improving system transparency, increasing user satisfaction, and facilitating human-agent collaboration. However, existing works on explainable reinforcement learning mostly focus on the single-agent setting and are not suitable for addressing challenges posed by multi-agent environments. We present novel methods to generate two types of policy explanations for MARL: (i) policy summarization about the agent cooperation and task sequence, and (ii) language explanations to answer queries about agent behavior. Experimental results on three MARL domains demonstrate the scalability of our methods. A user study shows that the generated explanations significantly improve user performance and increase subjective ratings on metrics such as user satisfaction.  ( 2 min )

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world. In several domains, such as healthcare, such technology has significant potential to reduce the burden on humans by providing quality assistance at scale. However, current methods rely on the traditional pipeline of predicting labels from data, thus completely ignoring the process and guidelines used to obtain the labels. Furthermore, post hoc explanations on the data to label prediction using explainable AI (XAI) models, while satisfactory to computer scientists, leave much to be desired to the end-users due to lacking explanations of the process in terms of human-understandable concepts. We \textit{introduce}, \textit{formalize}, and \textit{develop} a novel Artificial Intelligence (A) paradigm -- Process Knowledge-infused Learning (PK-iL). PK-iL utilizes a structured process knowledge that explicitly explains the underlying prediction process that makes sense to end-users. The qualitative human evaluation confirms through a annotator agreement of 0.72, that humans are understand explanations for the predictions. PK-iL also performs competitively with the state-of-the-art (SOTA) baselines.  ( 2 min )

Abusive language is a growing concern in many social media platforms. Repeated exposure to abusive speech has created physiological effects on the target users. Thus, the problem of abusive language should be addressed in all forms for online peace and safety. While extensive research exists in abusive speech detection, most studies focus on English. Recently, many smearing incidents have occurred in India, which provoked diverse forms of abusive speech in online space in various languages based on the geographic location. Therefore it is essential to deal with such malicious content. In this paper, to bridge the gap, we demonstrate a large-scale analysis of multilingual abusive speech in Indic languages. We examine different interlingual transfer mechanisms and observe the performance of various multilingual models for abusive speech detection for eight different Indic languages. We also experiment to show how robust these models are on adversarial attacks. Finally, we conduct an in-depth error analysis by looking into the models' misclassified posts across various settings. We have made our code and models public for other researchers.  ( 2 min )

We introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning technique) on time series data. A second round of Diffusion Maps on those latent coordinates allows the approximation of the reduced dynamical models. This second round enables mapping the latent space coordinates back to the full ambient space (what is called lifting); it also enables the approximation of full state functions of interest in terms of the reduced coordinates. In our work, we develop and test three different reduced numerical simulation methodologies, either through pre-tabulation in the latent space and integration on the fly or by going back and forth between the ambient space and the latent space. The data-driven latent space simulation results, based on the three different approaches, are validated through (a) the latent space observation of the full simulation through the Nystr\"om Extension formula, or through (b) lifting the reduced trajectory back to the full ambient space, via Latent Harmonics. Latent space modeling often involves additional regularization to favor certain properties of the space over others, and the mapping back to the ambient space is then constructed mostly independently from these properties; here, we use the same data-driven approach to construct the latent space and then map back to the ambient space.  ( 2 min )

In this paper, we utilize a machine learning approach to identify the significant pathways for c-di-GMP signaling proteins. The dataset involves gene counts from 12 pathways and 5 essential c-di-GMP binding domains for 1024 bacterial genomes. Two novel approaches, Least absolute shrinkage and selection operator (Lasso) and Random forests, have been applied for analyzing and modeling the dataset. Both approaches show that bacterial chemotaxis is the most essential pathway for c-di-GMP encoding domains. Though popular for feature selection, the strong regularization of Lasso method fails to associate any pathway to MshE domain. Results from the analysis may help to understand and emphasize the supporting pathways involved in bacterial cellulose production. These findings demonstrate the need for a chassis to restrict the behavior or functionality by deactivating the selective pathways in cellulose production.  ( 2 min )

In pathology, tissue samples are assessed using multiple staining techniques to enhance contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN based graph fusion approach that leverages complementary information from multiple non-registered histopathology images to predict pathologic scores. We demonstrate this approach in nonalcoholic steatohepatitis (NASH) by predicting CRN fibrosis stage and NAFLD Activity Score (NAS). Primary assessment of NASH typically requires liver biopsy evaluation on two histological stains: Trichrome (TC) and hematoxylin and eosin (H&E). Our multimodal approach learns to extract complementary information from TC and H&E graphs corresponding to each stain while simultaneously learning an optimal policy to combine this information. We report up to 20% improvement in predicting fibrosis stage and NAS component grades over single-stain modeling approaches, measured by computing linearly weighted Cohen's kappa between machine-derived vs. pathologist consensus scores. Broadly, this paper demonstrates the value of leveraging diverse pathology images for improved ML-powered histologic assessment.  ( 2 min )

Many neural-based recommender systems were proposed in recent years and part of them used Generative Adversarial Networks (GAN) to model user-item interactions. However, the exploration of Wasserstein GAN with Gradient Penalty (WGAN-GP) on recommendation has received relatively less scrutiny. In this paper, we focus on two questions: 1- Can we successfully apply WGAN-GP on recommendation and does this approach give an advantage compared to the best GAN models? 2- Are GAN-based recommender systems relevant? To answer the first question, we propose a recommender system based on WGAN-GP called CFWGAN-GP which is founded on a previous model (CFGAN). We successfully applied our method on real-world datasets on the top-k recommendation task and the empirical results show that it is competitive with state-of-the-art GAN approaches, but we found no evidence of significant advantage of using WGAN-GP instead of the original GAN, at least from the accuracy point of view. As for the second question, we conduct a simple experiment in which we show that a well-tuned conceptually simpler method outperforms GAN-based models by a considerable margin, questioning the use of such models.  ( 2 min )

In this paper, we explore the problem of deep multi-view subspace clustering framework from an information-theoretic point of view. We extend the traditional information bottleneck principle to learn common information among different views in a self-supervised manner, and accordingly establish a new framework called Self-supervised Information Bottleneck based Multi-view Subspace Clustering (SIB-MSC). Inheriting the advantages from information bottleneck, SIB-MSC can learn a latent space for each view to capture common information among the latent representations of different views by removing superfluous information from the view itself while retaining sufficient information for the latent representations of other views. Actually, the latent representation of each view provides a kind of self-supervised signal for training the latent representations of other views. Moreover, SIB-MSC attempts to learn the other latent space for each view to capture the view-specific information by introducing mutual information based regularization terms, so as to further improve the performance of multi-view subspace clustering. To the best of our knowledge, this is the first work to explore information bottleneck for multi-view subspace clustering. Extensive experiments on real-world multi-view data demonstrate that our method achieves superior performance over the related state-of-the-art methods.  ( 2 min )

Federated learning reduces the risk of information leakage, but remains vulnerable to attacks. We investigate how several neural network design decisions can defend against gradients inversion attacks. We show that overlapping gradients provides numerical resistance to gradient inversion on the highly vulnerable dense layer. Specifically, we propose to leverage batching to maximise mixing of gradients by choosing an appropriate loss function and drawing identical labels. We show that otherwise it is possible to directly recover all vectors in a mini-batch without any numerical optimisation due to the de-mixing nature of the cross entropy loss. To accurately assess data recovery, we introduce an absolute variation distance (AVD) metric for information leakage in images, derived from total variation. In contrast to standard metrics, e.g. Mean Squared Error or Structural Similarity Index, AVD offers a continuous metric for extracting information in noisy images. Finally, our empirical results on information recovery from various inversion attacks and training performance supports our defense strategies. These strategies are also shown to be useful for deep convolutional neural networks such as LeNET for image recognition. We hope that this study will help guide the development of further strategies that achieve a trustful federation policy.  ( 2 min )

Artificial Intelligence (AI) has found its applications in a variety of environments ranging from data science to cybersecurity. AI helps break through the limitations of traditional algorithms and provides more efficient and flexible methods for solving problems. In this paper, we focus on the applications of artificial intelligence in authentication, which is used in a wide range of scenarios including facial recognition to access buildings, keystroke dynamics to unlock smartphones. With the emerging AI-assisted authentication schemes, our comprehensive survey provides an overall understanding on a high level, which paves the way for future research in this area. In contrast to other relevant surveys, our research is the first of its kind to focus on the roles of AI in authentication.  ( 2 min )

Federated Learning (FL) has recently made significant progress as a new machine learning paradigm for privacy protection. Due to the high communication cost of traditional FL, one-shot federated learning is gaining popularity as a way to reduce communication cost between clients and the server. Most of the existing one-shot FL methods are based on Knowledge Distillation; however, distillation based approach requires an extra training phase and depends on publicly available data sets. In this work, we consider a novel and challenging setting: performing a single round of parameter aggregation on the local models without server-side training on a public data set. In this new setting, we propose an effective algorithm for Model Aggregation via Exploring Common Harmonized Optima (MA-Echo), which iteratively updates the parameters of all local models to bring them close to a common low-loss area on the loss surface, without harming performance on their own data sets at the same time. Compared to the existing methods, MA-Echo can work well even in extremely non-identical data distribution settings where the support categories of each local model have no overlapped labels with those of the others. We conduct extensive experiments on two popular image classification data sets to compare the proposed method with existing methods and demonstrate the effectiveness of MA-Echo, which clearly outperforms the state-of-the-arts.  ( 2 min )

Practical use of neural networks often involves requirements on latency, energy and memory among others. A popular approach to find networks under such requirements is through constrained Neural Architecture Search (NAS). However, previous methods use complicated predictors for the accuracy of the network. Those predictors are hard to interpret and sensitive to many hyperparameters to be tuned, hence, the resulting accuracy of the generated models is often harmed. In this work we resolve this by introducing Bilinear Interpretable Neural Architecture Search (BINAS), that is based on an accurate and simple bilinear formulation of both an accuracy estimator and the expected resource requirement, together with a scalable search method with theoretical guarantees. The simplicity of our proposed estimator together with the intuitive way it is constructed bring interpretability through many insights about the contribution of different design choices. For example, we find that in the examined search space, adding depth and width is more effective at deeper stages of the network and at the beginning of each resolution stage. Our experiments show that BINAS generates comparable to or better architectures than other state-of-the-art NAS methods within a reduced marginal search cost, while strictly satisfying the resource constraints.  ( 2 min )

In data processing and machine learning, an important challenge is to recover and exploit models that can represent accurately the data. We consider the problem of recovering Gaussian mixture models from datasets. We investigate symmetric tensor decomposition methods for tackling this problem, where the tensor is built from empirical moments of the data distribution. We consider identifiable tensors, which have a unique decomposition, showing that moment tensors built from spherical Gaussian mixtures have this property. We prove that symmetric tensors with interpolation degree strictly less than half their order are identifiable and we present an algorithm, based on simple linear algebra operations, to compute their decomposition. Illustrative experimentations show the impact of the tensor decomposition method for recovering Gaussian mixtures, in comparison with other state-of-the-art approaches.  ( 2 min )

In this paper, we utilize a machine learning approach to identify the significant pathways for c-di-GMP signaling proteins. The dataset involves gene counts from 12 pathways and 5 essential c-di-GMP binding domains for 1024 bacterial genomes. Two novel approaches, Least absolute shrinkage and selection operator (Lasso) and Random forests, have been applied for analyzing and modeling the dataset. Both approaches show that bacterial chemotaxis is the most essential pathway for c-di-GMP encoding domains. Though popular for feature selection, the strong regularization of Lasso method fails to associate any pathway to MshE domain. Results from the analysis may help to understand and emphasize the supporting pathways involved in bacterial cellulose production. These findings demonstrate the need for a chassis to restrict the behavior or functionality by deactivating the selective pathways in cellulose production.  ( 2 min )

It has been widely documented that the sampling and resampling steps in particle filters cannot be differentiated. The {\itshape reparameterisation trick} was introduced to allow the sampling step to be reformulated into a differentiable function. We extend the {\itshape reparameterisation trick} to include the stochastic input to resampling therefore limiting the discontinuities in the gradient calculation after this step. Knowing the gradients of the prior and likelihood allows us to run particle Markov Chain Monte Carlo (p-MCMC) and use the No-U-Turn Sampler (NUTS) as the proposal when estimating parameters. We compare the Metropolis-adjusted Langevin algorithm (MALA), Hamiltonian Monte Carlo with different number of steps and NUTS. We consider two state-space models and show that NUTS improves the mixing of the Markov chain and can produce more accurate results in less computational time.  ( 2 min )

We propose a population-level analysis to address issues of data sparsity when building predictive models of engineering infrastructure. By sharing information between similar assets, hierarchical Bayesian modelling is used to improve the survival analysis of a truck fleet (hazard curves) and power prediction in a wind farm (power curves). In each example, a set of correlated functions are learnt over the asset fleet, in a combined inference, to learn a population model. Parameter estimation is improved when sub-fleets of assets are allowed to share correlated information at different levels in the hierarchy. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The correlations can be inspected to inform which assets share information for which effect (i.e. parameter).  ( 2 min )

Expressing a matrix as the sum of a low-rank matrix plus a sparse matrix is a flexible model capturing global and local features in data popularized as Robust PCA (Candes et al., 2011; Chandrasekaran et al., 2009). Compressed sensing, matrix completion, and their variants (Eldar and Kutyniok, 2012; Foucart and Rauhut, 2013) have established that data satisfying low complexity models can be efficiently measured and recovered from a number of measurements proportional to the model complexity rather than the ambient dimension. This manuscript develops similar guarantees showing that $m\times n$ matrices that can be expressed as the sum of a rank-$r$ matrix and a $s$-sparse matrix can be recovered by computationally tractable methods from $\mathcal{O}(r(m+n-r)+s)\log(mn/s)$ linear measurements. More specifically, we establish that the low-rank plus sparse matrix set is closed provided the incoherence of the low-rank component is upper bounded as $\mu<\sqrt{mn}/(r\sqrt{s})$, and subsequently, the restricted isometry constants for the aforementioned matrices remain bounded independent of problem size provided $p/mn$, $s/p$, and $r(m+n-r)/p$ remain fixed. Additionally, we show that semidefinite programming and two hard threshold gradient descent algorithms, NIHT and NAHT, converge to the measured matrix provided the measurement operator's RIC's are sufficiently small. These results also provably solve convex and non-convex formulation of Robust PCA with the asymptotically optimal fraction of corruptions $\alpha=\mathcal{O}\left(1/(\mu r) \right)$, where $s = \alpha^2 mn$, and improve the previously best known guarantees by not requiring that the fraction of corruptions is spread in every column and row by being upper bounded by $\alpha$. Numerical experiments illustrating these results are shown for synthetic problems, dynamic-foreground/static-background separation, and multispectral imaging.  ( 2 min )

Consider the following distributed optimization scenario. A worker has access to training data that it uses to compute the gradients while a server decides when to stop iterative computation based on its target accuracy or delay constraints. The server receives all its information about the problem instance from the worker via a rate-limited noiseless communication channel. We introduce the principle we call Differential Quantization (DQ) that prescribes compensating the past quantization errors to direct the descent trajectory of a quantized algorithm towards that of its unquantized counterpart. Assuming that the objective function is smooth and strongly convex, we prove that Differentially Quantized Gradient Descent (DQ-GD) attains a linear contraction factor of $\max\{\sigma_{\mathrm{GD}}, \rho_n 2^{-R}\}$, where $\sigma_{\mathrm{GD}}$ is the contraction factor of unquantized gradient descent (GD), $\rho_n \geq 1$ is the covering efficiency of the quantizer, and $R$ is the bitrate per problem dimension $n$. Thus at any $R\geq\log_2 \rho_n /\sigma_{\mathrm{GD}}$ bits, the contraction factor of DQ-GD is the same as that of unquantized GD, i.e., there is no loss due to quantization. We show that no algorithm within a certain class can converge faster than $\max\{\sigma_{\mathrm{GD}}, 2^{-R}\}$. Since quantizers exist with $\rho_n \to 1$ as $n \to \infty$ (Rogers, 1963), this means that DQ-GD is asymptotically optimal. The principle of differential quantization continues to apply to gradient methods with momentum such as Nesterov's accelerated gradient descent, and Polyak's heavy ball method. For these algorithms as well, if the rate is above a certain threshold, there is no loss in contraction factor obtained by the differentially quantized algorithm compared to its unquantized counterpart. Experimental results on least-squares problems validate our theoretical analysis.  ( 3 min )

Class imbalance distribution widely exists in real-world engineering. However, the mainstream optimization algorithms that seek to minimize error will trap the deep learning model in sub-optimums when facing extreme class imbalance. It seriously harms the classification precision, especially on the minor classes. The essential reason is that the gradients of the classifier weights are imbalanced among the components from different classes. In this paper, we propose Attraction-Repulsion-Balanced Loss (ARB-Loss) to balance the different components of the gradients. We perform experiments on the large-scale classification and segmentation datasets and our ARB-Loss can achieve state-of-the-art performance via only one-stage training instead of 2-stage learning like nowadays SOTA works.  ( 2 min )

We study a family of adversarial multiclass classification problems and provide equivalent reformulations in terms of: 1) a family of generalized barycenter problems introduced in the paper and 2) a family of multimarginal optimal transport problems where the number of marginals is equal to the number of classes in the original classification problem. These new theoretical results reveal a rich geometric structure of adversarial learning problems in multiclass classification and extend recent results restricted to the binary classification setting. A direct computational implication of our results is that by solving either the barycenter problem and its dual, or the MOT problem and its dual, we can recover the optimal robust classification rule and the optimal adversarial strategy for the original adversarial problem. Examples with synthetic and real data illustrate our results.

We introduce a data-driven approach to building reduced dynamical models through manifold learning; the reduced latent space is discovered using Diffusion Maps (a manifold learning technique) on time series data. A second round of Diffusion Maps on those latent coordinates allows the approximation of the reduced dynamical models. This second round enables mapping the latent space coordinates back to the full ambient space (what is called lifting); it also enables the approximation of full state functions of interest in terms of the reduced coordinates. In our work, we develop and test three different reduced numerical simulation methodologies, either through pre-tabulation in the latent space and integration on the fly or by going back and forth between the ambient space and the latent space. The data-driven latent space simulation results, based on the three different approaches, are validated through (a) the latent space observation of the full simulation through the Nystr\"om Extension formula, or through (b) lifting the reduced trajectory back to the full ambient space, via Latent Harmonics. Latent space modeling often involves additional regularization to favor certain properties of the space over others, and the mapping back to the ambient space is then constructed mostly independently from these properties; here, we use the same data-driven approach to construct the latent space and then map back to the ambient space.

We study the adversarial bandit problem with composite anonymous delayed feedback. In this setting, losses of an action are split into $d$ components, spreading over consecutive rounds after the action is chosen. And in each round, the algorithm observes the aggregation of losses that come from the latest $d$ rounds. Previous works focus on oblivious adversarial setting, while we investigate the harder non-oblivious setting. We show non-oblivious setting incurs $\Omega(T)$ pseudo regret even when the loss sequence is bounded memory. However, we propose a wrapper algorithm which enjoys $o(T)$ policy regret on many adversarial bandit problems with the assumption that the loss sequence is bounded memory. Especially, for $K$-armed bandit and bandit convex optimization, we have $\mathcal{O}(T^{2/3})$ policy regret bound. We also prove a matching lower bound for $K$-armed bandit. Our lower bound works even when the loss sequence is oblivious but the delay is non-oblivious. It answers the open problem proposed in \cite{wang2021adaptive}, showing that non-oblivious delay is enough to incur $\tilde{\Omega}(T^{2/3})$ regret.

This paper compares the performances of three supervised machine learning algorithms in terms of predictive ability and model interpretation on structured or tabular data. The algorithms considered were scikit-learn implementations of extreme gradient boosting machines (XGB) and random forests (RFs), and feedforward neural networks (FFNNs) from TensorFlow. The paper is organized in a findings-based manner, with each section providing general conclusions supported by empirical results from simulation studies that cover a wide range of model complexity and correlation structures among predictors. We considered both continuous and binary responses of different sample sizes. Overall, XGB and FFNNs were competitive, with FFNNs showing better performance in smooth models and tree-based boosting algorithms performing better in non-smooth models. This conclusion held generally for predictive performance, identification of important variables, and determining correct input-output relationships as measured by partial dependence plots (PDPs). FFNNs generally had less over-fitting, as measured by the difference in performance between training and testing datasets. However, the difference with XGB was often small. RFs did not perform well in general, confirming the findings in the literature. All models exhibited different degrees of bias seen in PDPs, but the bias was especially problematic for RFs. The extent of the biases varied with correlation among predictors, response type, and data set sample size. In general, tree-based models tended to over-regularize the fitted model in the tails of predictor distributions. Finally, as to be expected, performances were better for continuous responses compared to binary data and with larger samples.

Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods generally assume at least one of (i) source and target task label spaces must overlap, (ii) source datasets are available, and (iii) target network architectures are consistent with source ones. However, these all assumptions are difficult to hold in practical settings because the target task rarely has the same labels as the source task, the source dataset access is restricted due to licensing and storage costs, and the target architecture is often specialized to each task. To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a target architecture with a synthesized dataset by using conditional source generative models. P-SSL applies SSL algorithms to labeled target data and unlabeled pseudo samples, which are generated by cascading the source classifier and generative models to condition them with target samples. Our experimental results indicate that our method can outperform baselines of scratch training and knowledge distillation.

A particular direction of recent advance about stochastic deep-learning algorithms has been about uncovering a rather mysterious heavy-tailed nature of the stationary distribution of these algorithms, even when the data distribution is not so. Moreover, the heavy-tail index is known to show interesting dependence on the input dimension of the net, the mini-batch size and the step size of the algorithm. In this short note, we undertake an experimental study of this index for S.G.D. while training a $\relu$ gate (in the realizable and in the binary classification setup) and for a variant of S.G.D. that was proven in Karmakar and Mukherjee (2022) for ReLU realizable data. From our experiments we conjecture that these two algorithms have similar heavy-tail behaviour on any data where the latter can be proven to converge. Secondly, we demonstrate that the heavy-tail index of the late time iterates in this model scenario has strikingly different properties than either what has been proven for linear hypothesis classes or what has been previously demonstrated for large nets.

In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and Hinf-norm based correction for a linear Gaussian system. As the dimension of state or parameter space grows, performing the full Kalman update with the dense covariance matrix for a large scale system requires increased storage and computational complexity, making it impractical. The VIF approach, based on mean-field Gaussian variational inference, reduces this burden through the variational approximation to the covariance usually in the form of a diagonal covariance approximation. The challenge is to retain convergence and correct for biases introduced by the sequential VIF steps. We desire a framework that improves feasibility while still maintaining reasonable proximity to the optimal Kalman filter as data is assimilated. To accomplish this goal, a Hinf-norm based optimization perturbs the VIF covariance matrix to improve robustness. This yields a novel VIF- Hinf recursion that employs consecutive variational inference and Hinf based optimization steps. We explore the development of this method and investigate a numerical example to illustrate the effectiveness of the proposed filter.  ( 2 min )

Allocation strategies improve the efficiency of crowdsourcing by decreasing the work needed to complete individual tasks accurately. However, these algorithms introduce bias by preferentially allocating workers onto easy tasks, leading to sets of completed tasks that are no longer representative of all tasks. This bias challenges inference of problem-wide properties such as typical task difficulty or crowd properties such as worker completion times, important information that goes beyond the crowd responses themselves. Here we study inference about problem properties when using an allocation algorithm to improve crowd efficiency. We introduce Decision-Explicit Probability Sampling (DEPS), a novel method to perform inference of problem properties while accounting for the potential bias introduced by an allocation strategy. Experiments on real and synthetic crowdsourcing data show that DEPS outperforms baseline inference methods while still leveraging the efficiency gains of the allocation method. The ability to perform accurate inference of general properties when using non-representative data allows crowdsourcers to extract more knowledge out of a given crowdsourced dataset.  ( 2 min )

We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature. Following the seminal paper of Foster and Vohra (1998), nature is often modeled as an adaptive adversary who sees all activity of the forecaster except the randomization that the forecaster may deploy. A number of papers have proposed randomized forecasting strategies that achieve an $\epsilon$-calibration error rate of $O(1/\sqrt{T})$, which we prove is tight in general. On the other hand, it is well known that it is not possible to be calibrated without randomization, or if nature also sees the forecaster's randomization; in both cases the calibration error could be $\Omega(1)$. Inspired by the equally seminal works on the "power of two choices" and imprecise probability theory, we study a small variant of the standard online calibration problem. The adversary gives the forecaster the option of making two nearby probabilistic forecasts, or equivalently an interval forecast of small width, and the endpoint closest to the revealed outcome is used to judge calibration. This power of two choices, or imprecise forecast, accords the forecaster with significant power -- we show that a faster $\epsilon$-calibration rate of $O(1/T)$ can be achieved even without deploying any randomization.  ( 2 min )

Building on (Neal and Hinton, 1998), where the problem tackled by EM is recast as the optimization of a free energy functional on an infinite-dimensional space, we obtain three practical particle-based alternatives to EM applicable to broad classes of models. All three are derived through straightforward discretizations of gradient flows associated with the functional. The novel algorithms scale well to high-dimensional settings and outperform existing state-of-the-art methods in numerical experiments.  ( 2 min )

VoiceFilter-Lite is a speaker-conditioned voice separation model that plays a crucial role in improving speech recognition and speaker verification by suppressing overlapping speech from non-target speakers. However, one limitation of VoiceFilter-Lite, and other speaker-conditioned speech models in general, is that these models are usually limited to a single target speaker. This is undesirable as most smart home devices now support multiple enrolled users. In order to extend the benefits of personalization to multiple users, we previously developed an attention-based speaker selection mechanism and applied it to VoiceFilter-Lite. However, the original multi-user VoiceFilter-Lite model suffers from significant performance degradation compared with single-user models. In this paper, we devised a series of experiments to improve the multi-user VoiceFilter-Lite model. By incorporating a dual learning rate schedule and by using feature-wise linear modulation (FiLM) to condition the model with the attended speaker embedding, we successfully closed the performance gap between multi-user and single-user VoiceFilter-Lite models on single-speaker evaluations. At the same time, the new model can also be easily extended to support any number of users, and significantly outperforms our previously published model on multi-speaker evaluations.  ( 2 min )

Variable-density cellular structures can overcome connectivity and manufacturability issues of topologically optimized structures, particularly those represented as discrete density maps. However, the optimization of such cellular structures is challenging due to the multiscale design problem. Past work addressing this problem generally either only optimizes the volume fraction of single-type unit cells but ignores the effects of unit cell geometry on properties, or considers the geometry-property relation but builds this relation via heuristics. In contrast, we propose a simple yet more principled way to accurately model the property to geometry mapping using a conditional deep generative model, named Inverse Homogenization Generative Adversarial Network (IH-GAN). It learns the conditional distribution of unit cell geometries given properties and can realize the one-to-many mapping from properties to geometries. We further reduce the complexity of IH-GAN by using the implicit function parameterization to represent unit cell geometries. Results show that our method can 1) generate various unit cells that satisfy given material properties with high accuracy ($R^2$-scores between target properties and properties of generated unit cells $>98\%$) and 2) improve the optimized structural performance over the conventional variable-density single-type structure. In the minimum compliance example, our IH-GAN generated structure achieves a $79.7\%$ reduction in concentrated stress and an extra $3.03\%$ reduction in displacement. In the target deformation examples, our IH-GAN generated structure reduces the target matching error by $86.4\%$ and $79.6\%$ for two test cases, respectively. We also demonstrated that the connectivity issue for multi-type unit cells can be solved by transition layer blending.  ( 3 min )

The article is concerned with the problem of multi-step financial time series forecasting of Foreign Exchange (FX) rates. To address this problem, we introduce a parameter-free regression network termed RegPred Net. The exchange rate to forecast is treated as a stochastic process. It is assumed to follow a generalization of Brownian motion and the mean-reverting process referred to as the generalized Ornstein-Uhlenbeck (OU) process, with time-dependent coefficients. Using past observed values of the input time series, these coefficients can be regressed online by the cells of the first half of the network (Reg). The regressed coefficients depend only on - but are very sensitive to - a small number of hyperparameters required to be set by a global optimization procedure for which, Bayesian optimization is an adequate heuristic. Thanks to its multi-layered architecture, the second half of the regression network (Pred) can project time-dependent values for the OU process coefficients and generate realistic trajectories of the time series. Predictions can be easily derived in the form of expected values estimated by averaging values obtained by Monte Carlo simulation. The forecasting accuracy on a 100 days horizon is evaluated for several of the most important FX rates such as EUR/USD, EUR/CNY, and EUR/GBP. Our experimental results show that the RegPred Net significantly outperforms ARMA, ARIMA, LSTMs, and Autoencoder-LSTM models in this task.  ( 2 min )

Statistical Inference is the process of determining a probability distribution over the space of parameters of a model given a data set. As more data becomes available this probability distribution becomes updated via the application of Bayes' theorem. We present a treatment of this Bayesian updating process as a continuous dynamical system. Statistical inference is then governed by a first order differential equation describing a trajectory or flow in the information geometry determined by a parametric family of models. We solve this equation for some simple models and show that when the Cram\'{e}r-Rao bound is saturated the learning rate is governed by a simple $1/T$ power-law, with $T$ a time-like variable denoting the quantity of data. The presence of hidden variables can be incorporated in this setting, leading to an additional driving term in the resulting flow equation. We illustrate this with both analytic and numerical examples based on Gaussians and Gaussian Random Processes and inference of the coupling constant in the 1D Ising model. Finally we compare the qualitative behaviour exhibited by Bayesian flows to the training of various neural networks on benchmarked data sets such as MNIST and CIFAR10 and show how that for networks exhibiting small final losses the simple power-law is also satisfied.  ( 2 min )

To act safely and ethically in the real world, agents must be able to reason about harm and avoid harmful actions. In this paper we develop the first statistical definition of harm and a framework for factoring harm into algorithmic decisions. We argue that harm is fundamentally a counterfactual quantity, and show that standard machine learning algorithms are guaranteed to pursue harmful policies in certain environments. To resolve this, we derive a family of counterfactual objective functions that robustly mitigate for harm. We demonstrate our approach with a statistical model for identifying optimal drug doses. While identifying optimal doses using the causal treatment effect results in harmful treatment decisions, our counterfactual algorithm identifies doses that are far less harmful without sacrificing efficacy. Our results show that counterfactual reasoning is a key ingredient for safe and ethical AI.  ( 2 min )

Hello, I have a problem regarding my auto-encoder model. I built it so it can be able to give a MSE error abnormally high when presented an image too different from what it knows. I have this little function at the end of my program to predict whether or not the image presented is an anomaly : ` def check_anomaly(img_path): density_threshold = 2500 #Set this value based on the above exercise reconstruction_error_threshold = 0.004 # Set this value based on the above exercise img = Image.open(img_path) img = np.array(img.resize((128,128), Image.ANTIALIAS)) plt.imshow(img) img = img / 255. img = img[np.newaxis, :,:,:] encoded_img = encoder_model.predict([[img]]) encoded_img = [np.reshape(img, (out_vector_shape)) for img in encoded_img] density = kde.score_samples(encoded_img)[0] reconstruc…  ( 1 min )

I've also noticed Cloudera and DataRobot seem to follow user pricing where DataBricks follows the standard hourly machine rate. submitted by /u/SmarterChild8675309 [link] [comments]

I have a data set of satellite images of aircraft. For the labels of these images I have the bounding boxes for all aircraft in that image. I looking for some advice on where to get started for this. I need to estimate all bounding boxes for these aircraft, any suggestions? submitted by /u/The_Dov [link] [comments]

Hi all, does anyone have experience with automatic taxonomy/ontology extraction from unlabelled corpus, especially how to evaluate the extracted structures without gold standards? most published papers would invite students/researchers to conduct manual reviews, thus making it very difficult to compare the results. thanks in advance. submitted by /u/CestLucas [link] [comments]

I am evaluating the performance of a model. I get different Precison-Recall curves for different configurations and the best F1 score (0.56) corresponds to the red point. Would you consider this performance acceptable? I know there is a lot of room for improvement but is it okay or is it extremely poor performance? https://preview.redd.it/k5qihccdb5w81.png?width=784&format=png&auto=webp&s=a0f3408f2ff671ee491faa50752b6c8e0cf17e4c Also, if I understood it correctly, each point of the Precision-Recall curve has its own F1 Score, right? Thank you! submitted by /u/SeaResponsibility176 [link] [comments]  ( 1 min )

I've made surgeon https://github.com/archinetai/surgeon-pytorch, a small library to inspect the intermediate output layers of pyTorch models without changing the original implementation. This can be very useful if you are using pre-trained models (e.g. from Huggingface or torch.hub) and want to get embeddings, attention matrices, or simply debug the model without adding additional code – which is often hard to do without changing the implementation. I hope this can be helpful to anyone! submitted by /u/Aglitter [link] [comments]  ( 1 min )

Lately, I have been receiving many messages from the organizers of this conference that they have some free passes to pass on to attend it: https://ai4.io/usa/ While the speaker line-up from the industry appears impressive, I have not heard of this conference before. Any thoughts on how legit/good this conference is? submitted by /u/roalddahl14 [link] [comments]  ( 1 min )

The D3VO paper from the Tech. University of Munich looks quite promising, however that Uni does not seem very keen on publishing code along with their papers. Has already tried to reproduce their work? What kind of results did you see in your version? What hardware did you run it on? How difficult was it reproduce their models in pytorch? submitted by /u/autojazari [link] [comments]  ( 1 min )

I stumbled upon GFlow Net and in my opinion, it looks very similar to diffusion models. There is a touch of RL in GFlow Net but the main idea is very similar to diffusion models. is that right? or am I missing something? submitted by /u/dimem16 [link] [comments]

Hello everyone, this post is about generative models! (i.e. Score-based-generative models, GANs, etc.) on leaderboards like this https://paperswithcode.com/sota/image-generation-on-cifar-10 How do they check if the models do not just memorize the training examples? The FID score would be optimal in case you would just generate training examples again. Best submitted by /u/future_gcp_poweruser [link] [comments]  ( 5 min )

It seems like the list of accepted blog posts has been published for the ICLR 2022 blog post track (https://iclr-blog-track.github.io/). They also invited Karpathy to publish his most recent blog post on the history and future of convnets. What do you think about the blog posts? Any blog posts that you particularly like or that are definitely worth reading? Any blog posts that actually have interesting contributions and/or that you plan to cite? But maybe more importantly: how do you think this will evolve? They seem to have decided to organise the blog post track again for next year's ICLR already. Do you think these kind of publications could have an impact on how we do science or is this rather a nice extra on top of scientific work? There has only been one post in this subreddit on one of the accepted blog posts (since the announcement). I think there are some nice blog posts in there, so I expected to find some discussions here, but it seems like it is either ignored or not worth discussing. Therefore, I thought it would be interesting to (try to) start a discussion. TL;DR: what do you think of the ICLR blog post track and/or the accepted blog posts? submitted by /u/mr_tsjolder [link] [comments]  ( 1 min )

I'm working on a ML model for data extraction out of documents. The model is trained and deployed into production. For fine tuning the model and improving performance I added the possibility for users to correct the extracted data. A concrete example would be: the model labels a word in the document as "company_name" the user corrects it to "street_name". This correction is then used to fine tune the model. Currently the fine tuning is done manually. That is, if the number of corrections exceeds some threshold I take them and start a new training and evaluate the new model before putting it into production. My question would be: is there an MLOps tool that automates this process? Or should I write one myself? I am aware of the tool seldon core that offers A/B testing for comparing the old model with the new one and putting it into production. But unfortunately it does not offer automatic fine tuning. Or that's what I understood from their website. submitted by /u/alzoubi36 [link] [comments]  ( 1 min )

Would you like to create an interactive presentation for your ML model directly from Jupyter Notebook? I'm working on an open-source project for converting notebooks into interactive documents. Recently, I've added the option to turn notebooks into interactive slides. You can showcase your Machine Learning model as an interactive presentation. During the presentation, the user can change values and recompute the slide! I've created a demo presentation where I use Random Forest to predict Iris species. The screenshot recording of the presentation: https://github.com/pplonski/ml-model-slides/raw/main/media/slides-from-ml-model.gif The presentation is available online (deployed at Heroku) https://ml-model-presentation.herokuapp.com/ What is more, the presentation code is on GitHub https://github.com/pplonski/ml-model-slides (yes, code is presentation! bye-bye PPT) In case anyone is interested, the framework is called Mercury. submitted by /u/pp314159 [link] [comments]  ( 1 min )

Hello, I’m working as a Data Scientist currently and I realized that most of my time is spent on feature engineering. My general practice is that I create aggregations of data (via sql because of the amount of data that needs to be processed) like sum, mean, avg, std, median, q25, q75. I need to do it on a few dozen features. Also I am calculating these aggregations on different time windows: previous week, previous month, previous 3 month. At the end I end up with hundreds of features and I need to select the ones that make any sense, contain relevant information. Currently I am applying pandas profiling, or sweetviz on this huge dataset and trying to analyze it by eyeballing the results. My main challenge is that this process is highly repetitive and manual. I am wondering if there is any tool out there that could help me automate this process and make some parts reusable? I like having a UI especially for visualizing the data. Am I doing something wrong or is there a tool that I’m clearly not aware of? submitted by /u/sgergely [link] [comments]  ( 4 min )

Hi, I am faced with something that I thought was simpe but I have been thinking about it so much that I now very confused. Any suggestions are helpful ! Let's assume the scenario that you have a database of cars. For each car you have a brand, model, colour etc. You have an api for this db which you can call, with a car id, and surface the car data. People rely on this api for fast and up to date car information. You have 1 million cars in the db. Assume now there is a requirement to predict a yes/no if the car has a black front bumper (completely made up requirement). This has to be surfaced to the api users along with the car data. You have built a classifier for this, and it takes some time to surface a prediction, e.g. 2 mins. You select what you think is the best operating point for your classifier. When a new car gets added into the db you can now run your predictor and you get back a probability and depending on the operating point a yes/no. What is the best approach here now ? Do you store the result of this prediction in the database (probability, model_version and boolean result) alongside the rest of the car data ? Less flexible as if you decide to tweak the operating point you will need to recalculate the new boolean values for all cars - but you have consistency wrt to what you have in the db and what you return to your users. If there is a mistake you can readily fix it by altering the boolean field. Do you just store the probabilities and decide on the yes/no using the model_version & operating point only when you surface the data to the end user ? More flexible if you decide to tweak the opearting point of the classifier. or would you have a completely different approach ? Would you change anything, if for some cars you have the ground truth data of they have a black bumper or not in the db already ? Thanks for reading ! submitted by /u/42isthenumber_ [link] [comments]  ( 2 min )

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Hello everyone, first of all sorry for my poor English. I am fairly new to Deep RL, and RL in general. I want to implement custom environment with multiple robots in it. Each robot would be given same task to do, so what I am trying to do is simulate parallelism. My question is: if i have 100 robots in simulation, do i have to instantiate 100 agents (neural networks) to control those robots or single network with batch size of 100 will suffice? Does batch of observations in anyway disturbs agent action (network) output? It would be much more memory efficient with single neural network. So far, I've seen that in process of acting in env agent takes observation of batch size 1 and outputs corresponding action for that observation. Since each observation from the batch is propagated trough the network without calculating gradient it should not be affected by the batch size. If anyone could explain to me if my way of thinking is wrong, and why. :) submitted by /u/Dexter_fixxor [link] [comments]  ( 1 min )

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i understand about why REINFORCE has high variance but how does AC mitigates it ? submitted by /u/aabra__ka__daabra [link] [comments]  ( 1 min )

I'm looking to find the concrete explanation and difference of on-policy and off-policy learning strategy if possible mathematics can alao be explained. submitted by /u/Western-Age3148 [link] [comments]  ( 1 min )

Feel free to apply! Algorithmenentwickler-AI-&-Machine-Learning-Motion-Planning submitted by /u/gab_ma [link] [comments]

We are currently offering Internships & Theses in the Field of Artificial Intelligence. Here are the links to our open positions. Feel free to share the post! ✌🏽 Mandatory Internship Software Development in the field of Artificial Intelligence Pflichtpraktikant Reinforcement Learning Algorithmen Pflichtpraktikum/ Masterarbeit: Reinforcement Learning submitted by /u/gab_ma [link] [comments]  ( 1 min )

What does the future of deep reinforcement learning look like? I feel like people were pretty hyped about it a year or two ago. Is it heavily researched now and expected to be used more in the future? What are some real world tasks that it can help with? I've seen it used a lot for playing games, self driving cars, and manufacturing robotics. Anything else that it can be applied to? Will it likely be used for autonomous robots when they become more common? What are some areas that can be improved upon? What are the most recent advancements and what is a good topic to focus on for future research? Thank you. submitted by /u/johnGettings [link] [comments]  ( 2 min )

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I've only every used writesonic.com I got premium acct access for free. I love it for when u need to quickly spin an article if I'm on a backlink building spree. Haven't tested against others but overall good, and improvements we being made on it all the time (in active development). submitted by /u/CliffWoolum [link] [comments]  ( 1 min )

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I was playing around with several AI models and this sort of stuff happens all the time. https://preview.redd.it/6arq56nih3w81.png?width=1163&format=png&auto=webp&s=5ca68dce977ae671b0b928c2f1ec3cdd8478257f I decided to prepare a compilation and rank the answers. Can you help me with deciding how bad or good are some of the answers by taking a survey here? Edit: I apologize if some of you felt tricked into completing the survey by the original version of the post. It does have about 50 questions but they are mostly just yes/no, good/bad, so it shouldn't take longer than 10 minutes. I intend to write a piece about how AI assistants are doing and prepare a compilation of AI fails. I will share the results :) submitted by /u/KazRainer [link] [comments]  ( 1 min )

Hi! Im looking for a dataset containing images of personal items (Wallet, keys, phone etc), annotated by bounding boxes. Cant seem to find anything, do anyone know of such a dataset? Thanks in advance! submitted by /u/ifinty [link] [comments]  ( 1 min )

I'm brand new to programming. I am thinking I need a bot to skim through pdf's and through trial and error, I can take the pdf's and make the bot come up with suggestions to scripts or books.However I need to use an AI which already has training in English grammatical and sentence structure and langue/information flow. not nescceecarily interpretation meaning or philosophy or other higher levels of language proficiency. Say I wanted to write a novel about sailors. I'd let it skim some 100 novels on sailing, and generate inputs. then I'd match it up against a separate set of Ai to fact check each other. or even with form blogs from some sailing subreddits or news feeds. I could do this with any topic. medicine, engineering, biology, drama novels etc. Are there any free/opensource bots who already know the English language and might read books and make suggestions based on guided inputs? submitted by /u/International__ [link] [comments]  ( 3 min )

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​ https://preview.redd.it/jbvwyd7u41w81.png?width=393&format=png&auto=webp&s=6cbd5568a84c4d96c03ca9a99bbed73b1576a1d9 any bots that can answer better? submitted by /u/MalwareLord [link] [comments]

I hear about people who trained or fed an AI bot to say, write a Biden Speech, or create poetry. I want to create a bot that pulls important numbers from my local community to post to social media — like lowest gas prices in town, weather average, mortgage interest rates and a couple other things. submitted by /u/No-Setting2541 [link] [comments]  ( 1 min )

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So, I'm not super comfortable with swearing, but there are a few YouTubers I follow that are as vulgar as they are hilarious. My plan, as it stands, is to use python in concert with some sort of generic voice to text software to get a list of timestamps for any F words, and then use that list of timestamps to generate an FFMPEG command to cut the audio at each timestamp and remerge into a new video. Does anyone have any recommendations for audio processing packages or better approaches? I'm keen to use anything with hardware acceleration (because making a script to abuse my shiny new graphics card is half my motivation). P.S. I haven't touched python much since graduating, can't wait to dig my claws back in. Although... if anyone has an audio processing package in C# that would be hecking cool too Have a nice day y'all submitted by /u/The_Real_Slim_Lemon [link] [comments]  ( 1 min )

Linking techniques from machine learning with advanced numerical simulations, MIT researchers take an important step in state-of-the-art predictions for fusion plasmas.  ( 6 min )

Drug discovery has come a long way from its roots in serendipity. It is now an increasingly rational process, in which one important phase, called lead optimization, is the stepwise search for promising drug candidate compounds in the lab. In this phase, expert medicinal chemists work to improve “hit” molecules—compounds that demonstrate some promising properties, […] The post MoLeR: Creating a path to more efficient drug design appeared first on Microsoft Research.  ( 6 min )

A hundred and forty turbines in the North Sea — and some GPUs in the cloud — pumped wind under the wings of David Standingford and Jamil Appa’s dream. As colleagues at a British aerospace firm, they shared a vision of starting a company to apply their expertise in high performance computing across many industries. Read article > The post Answers Blowin’ in the Wind: HPC Code Gives Renewable Energy a Lift appeared first on NVIDIA Blog.  ( 4 min )

Entrepreneur Jason Mars calls conversation our “first technology.” Before humans invented the wheel, crafted a spear or tamed fire, we mastered the superpower of talking to one another. That makes conversation an incredibly important tool. But if you’ve dealt with the automated chatbots deployed by the customer service arms of just about any big organization Read article > The post What Is Conversational AI? ZeroShot Bot CEO Jason Mars Explains appeared first on NVIDIA Blog.  ( 2 min )

TinyML is a groundbreaking technology! Possessing a lot of potential it is sure to grow exponentially in the coming years.  ( 3 min )

A couple days ago I wrote about homogeneous coordinates projective planes. I said that the lines y = 5 and y = 6 intersect in a point “at infinity.” In projective geometry any two distinct lines intersect in exactly one point, and you can compute that intersection point the same way, whether the intersection is […] Calculating where projective lines intersect first appeared on John D. Cook.  ( 4 min )

I recently started learning machine learning, and I have implemented several transformer models for different tasks from scratch in PyTorch in my Github repository: Transformer-Models-from-Scratch The notebooks are self-contained. And I also included a note I wrote on transformers. Hope it's helpful for anyone learning the transformer model! Let me know if you have any comments! submitted by /u/hbchen-one [link] [comments]

Hi all. I am starting a github repo to compile a list of company blogs about their MLops/ infra. Please feel free to contribute if you are interested: https://github.com/enochkan/awesome-ml-stack submitted by /u/kanxx030 [link] [comments]

I'd like to show off a TTS system I have been working on for the past year. I've open-sourced all the code and the trained model weights: https://github.com/neonbjb/tortoise-tts This was born out of a desire to reproduce the original DALLE with speech. It is "zero-shot" because you feed the text and examples of a voice to mimic as prompts to an autoregressive LLM. I think the results are fantastic. Here are some samples: https://nonint.com/static/tortoise_v2_examples.html Here is a colab in which you can try out the whole system: https://colab.research.google.com/drive/1wVVqUPqwiDBUVeWWOUNglpGhU3hg_cbR submitted by /u/neonbjb [link] [comments]  ( 1 min )

Hey, we've been building Baseten to be able quickly deploy models, backends and frontends. I'd love to get your feedback. submitted by /u/Available-Cookie2754 [link] [comments]

Hey folks, There’s an upcoming free talk on May 13. This is what I know: Vijay K, Head of Engineering at Scale.ai, and Mike Wu, Stanford PhD in Machine Learning are going to be talking about strategies for taking a data centric approach to AI, and Vijay’s lessons from doing this at Apple, YouTube, and Scale AI. There’s a a renewed focus on the data layer as a foundation for successful ML projects, and Vijay participated in this transformation firsthand. You’ll be able to hear his reflections and learnings, should be super useful! Sign-up link is here, see you there. submitted by /u/sb2nov [link] [comments]  ( 1 min )

This seems like a pretty powerhouse team adept.ai/post/introducing-adept submitted by /u/mrpogiface [link] [comments]

Hi, I was wondering how long it would take or if it would be possible for a model with 6 billion neurons to become able to predict that for 6 7 8 the next number should be 9, given only a single training example 1 2 3. Let's say we use an architecture like the ones used in GPT. Essentially, I am trying to make sense of something here: is the human design component in DL the weak link in the AGI chain? Much like we could not achieve a true AI by manually coding each rule with if's and else, perhaps we cannot achieve true intelligence by manually designing the networks. Can we grow a model organically so it immediately gets this right from a single training example, and continue using the same network to keep on learning from rich input and making new observations. For guidance, the network can use what it already knows, learning outward. A pre-programmed inherent ability to find motifs, for example with STUMPY and Time Series Analysis, allows it to make relationship observations, and is how the agent immediately guesses that a numeric pattern increments by 1 from a single training example. Putting this into DL, perhaps we can achieve something less organic but still good using a model-agnostic architecture with multi-resolution 'tiles' of neurons. Some property of the information would theoretically allow deciding if the tile should be upgraded to a higher resolution (more neurons), and a side buffer keeps track of the connections between these tiles and tries to move the topology forward, adding some small clusters, removing others, attempting to connect them, etc. submitted by /u/o_snake-monster_o_o_ [link] [comments]  ( 2 min )

Reading the original diffusion models paper and the improved diffusion model by openAI, I noticed they are using EMA (exponential moving average) to update the parameters of the models. so I started looking at the code openAI published for their version of the diffusion models, and when looking at the code, I see that the model during the training process has its params stored in a variable called "master_params" and then they create a deep copy of the params and call them ema_params. when looking at the "optimize_normal" method, I see that they update the model params using AdamW and gradient descent, and then after that, they update the ema param variable using the EMA equation, so that means the actual model params do a full gradient descent step to the reach minimum of the loss function, and then they do a pseudo step from the original parameters before the optimizer and making them closer to the params after the optimizer. but then looking at the rest of the code, all I see is that they just save a checkpoint of the ema params to the disk but never update the model params using them or anything. so my question is, what is the EMA for if it is not used during training and the model is fully updated using "classical" machine learning optimization with gradient descent? only at inference time do they load the EMA params to generate images, instead of the regular params that were updated using the AdamW? submitted by /u/eyalmazuz [link] [comments]  ( 2 min )

We've recently added Hugging Face support to https://github.com/graphsignal/graphsignal profiler, which I'd like to share in case someone finds it useful in their efforts to optimize speed and compute. More details, code and screenshots in the blog post https://graphsignal.com/blog/benchmarking-and-profiling-hugging-face-training-with-graphsignal/. submitted by /u/l0g1cs [link] [comments]

Researchers from MIT/Meta recently released a new framework for unsupervised sentence embedding. The performance seems to be better than SimCSE, the previous SOTA, by 2.3 absolute points on downstream tasks. The pretrained models are available on Huggingface. GitHub: https://github.com/voidism/DiffCSE arXiv: https://arxiv.org/abs/2204.10298 submitted by /u/virtualenv [link] [comments]  ( 1 min )

Some useful optional considerations: Assume copies of the original copyrighted work are owned lawfully. Assume the AI maintains a single active copy to avoid group performance. The implications this has on banning possibly uploading your own consciousness to save your life on copyright grounds. This “stealing” concept people jump to seems to bring up some interesting logical contradictions. What do you think? submitted by /u/sext-scientist [link] [comments]  ( 2 min )

Are you passionate about coding, data science or Earth observation? https://preview.redd.it/wfwk9ifo4vv81.png?width=1920&format=png&auto=webp&s=5f11885bd8efe88986c181b565cc160534634f0b We're looking for bright-minded people from around the world to showcase their skills and develop new Jupyter Notebooks using Copernicus data! Sound interesting? Find out more here: https://www.eumetsat.int/science-blog/new-jupyter-notebook-competition submitted by /u/EUMETSAT [link] [comments]

I have reached a blocking point in a current project. We are using K-Prototypes to segment two populations (one with 100k elements and the other with 1M). In order to evaluate the clustering, during our K-Means stage we used silhouettes which are already implemented in sklearn. For the second stage, this became a problem. Either it was a time issue or a memory issue. So, for the 100k dataset, finding the silhouette profile took around 50 hours using a custom distance metric function. While this is feasible in the project's scope, for the 1M dataset the computation time would be highly impractical. As such, at the moment, we are stuck without a proper evaluation metric for our model. Sure, we can run Davies-Bouldin or other similar metrics, but the silhouette profile gave us much more detailed information. We are also moving the project to databricks. At first, the pyspark clustering evaluator had me hopeful, but it has very limited options regarding distance metrics. This is also an issue because the model is to be deployed in production and should have some metric informing when it needs to be retrained. While this point is still fluid, this is the preferred course of action. Has anyone faced similar issues using K-Prototypes? Or just silhouette profiles with custom distance metrics? submitted by /u/CaptMartelo [link] [comments]  ( 1 min )

How important is clean data for how your AI models perform? According to our experiments - very important. Using state-of-the-art confidence learning to clean up PASCAL, two people improved our primary model metric by 13% in a week. To learn more about our results and what we did check out our article: https://hasty.ai/content-hub/articles/cleaning-pascal-improving-map-by-13?utm_source=mk832ksa Disclaimer: We used our own platform to clean up the data and the article, therefore, contains self-promotion. However, the article mainly focuses on the results we achieved. submitted by /u/treebeard_hasty_ai [link] [comments]  ( 3 min )

Hi. We recently did some work on using language models for molecule captioning and text-based molecule generation. You can think of it as doing translation between molecules and natural language. Would love to know if you have any feedback 🤗. Arxiv: https://arxiv.org/abs/2204.11817 submitted by /u/SimilarShape9122 [link] [comments]  ( 1 min )

“Diffusion models beat GANs”. While true, the statement comes with several ifs and buts, not to say that the math behind diffusion models is not for the faint of heart. Alas, GLIDE, an OpenAI paper from last December took a big step towards making it true in every sense. Specifically, it introduced a new guidance method for diffusion models that produces higher quality images than even DALL-E, which uses expensive CLIP reranking. And if that wasn’t impressive enough, GLIDE models can be fine-tuned for various downstream tasks such a inpainting and and text-based editing. As for the details, let’s dive in, shall we? Full summary: https://t.me/casual_gan/289 Blog post: https://www.casualganpapers.com/faster-diffusion-models-text-to-image-classifier-free-guidance/GLIDE-explained.html GLIDE arxiv / code Join the discord community and follow on Twitter for weekly AI paper summaries! submitted by /u/KirillTheMunchKing [link] [comments]  ( 1 min )

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I'm really new to neural networks and I was wondering how to speed up the process of selecting the best one when I do the training. What I mean is, among the training/validation/test quotas, the number of hidden layers, the type of activation functions, the size of each layers, how do I iterate to find the optimal combination without having to try every little mix? Is there a way to rank the impact of these 4 components on the mse and iterate one at a time to select each aspect? Thanks in advance submitted by /u/beppegrosso97 [link] [comments]  ( 1 min )

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Do you know reliable sites where to learn artificial intelligence? (i'm studying computer engineering) but i already want to study something in advance submitted by /u/oraudev [link] [comments]  ( 1 min )

https://blog.r2c.io/the-future-of-apps-intelligence/ submitted by /u/R2Consulting [link] [comments]

How important is clean data for how your AI models perform? According to our experiments - very important. Using state-of-the-art confidence learning to clean up PASCAL, two people improved our primary model metric by 13% in a week. To learn more about our results and what we did check out our article: https://hasty.ai/content-hub/articles/cleaning-pascal-improving-map-by-13?utm_source=da39a3ee Disclaimer: We used our own platform to clean up the data and the article, therefore, contains self-promotion. However, the article mainly focuses on the results we achieved. submitted by /u/treebeard_hasty_ai [link] [comments]  ( 1 min )

As featured on the Lex Fridman podcast, the Singularity weblog podcast and the Future of Life Institute podcast The channel features shorts of Joscha's opinions and perspectives edited from podcasts. You can check out the trailer, which mostly consists of podcast hosts' minds imploding. All channel videos Channel playlists Channel creator: /u/24karate Enjoy 🤖 submitted by /u/tasinet [link] [comments]  ( 1 min )

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“Diffusion models beat GANs”. While true, the statement comes with several ifs and buts, not to say that the math behind diffusion models is not for the faint of heart. Alas, GLIDE, an OpenAI paper from last December took a big step towards making it true in every sense. Specifically, it introduced a new guidance method for diffusion models that produces higher quality images than even DALL-E, which uses expensive CLIP reranking. And if that wasn’t impressive enough, GLIDE models can be fine-tuned for various downstream tasks such a inpainting and and text-based editing. As for the details, let’s dive in, shall we? Full summary: https://t.me/casual_gan/289 Blog post: https://www.casualganpapers.com/faster-diffusion-models-text-to-image-classifier-free-guidance/GLIDE-explained.html GLIDE arxiv / code Join the discord community and follow on Twitter for weekly AI paper summaries! submitted by /u/KirillTheMunchKing [link] [comments]  ( 1 min )

In this post, we demonstrate Kubeflow on AWS (an AWS-specific distribution of Kubeflow) and the value it adds over open-source Kubeflow through the integration of highly optimized, cloud-native, enterprise-ready AWS services. Kubeflow is the open-source machine learning (ML) platform dedicated to making deployments of ML workflows on Kubernetes simple, portable and scalable. Kubeflow provides many […]  ( 15 min )

In this post, we walk you through two sampling techniques in Amazon SageMaker Data Wrangler so you can quickly create processing workflows for your data. We cover both random sampling and stratified sampling techniques to help you sample your data based on your specific requirements. Data Wrangler reduces the time it takes to aggregate and […]  ( 7 min )

The adoption of AWS cloud technology at NatWest Group means moving our machine learning (ML) workloads to a more robust and scalable solution, while reducing our time-to-live to deliver the best products and services for our customers. In this cloud adoption journey, we selected the Customer Lifetime Value (CLV) model to migrate to AWS. The […]  ( 12 min )

This is the third post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. This post is intended for data scientists, MLOps engineers, and data engineers who are interested in building ML pipeline templates with Amazon SageMaker. […]  ( 8 min )

This is the second post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS Professional Services to build a new machine learning operations (MLOps) platform. In this post, we share how the NatWest Group utilized AWS to enable the self-service deployment of their standardized, secure, and compliant MLOps […]  ( 12 min )

This is the first post of a four-part series detailing how NatWest Group, a major financial services institution, partnered with AWS to build a scalable, secure, and sustainable machine learning operations (MLOps) platform. This initial post provides an overview of the AWS and NatWest Group joint team implemented Amazon SageMaker Studio as the standard for […]  ( 11 min )

Amazon SageMaker Data Wrangler is a new capability of Amazon SageMaker that helps data scientists and data engineers quickly and easily prepare data for machine learning (ML) applications using a visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code. Today, […]  ( 7 min )

Hello, I am working on compression of deep neural networks using reinforcement learning. There is one agent that learns to compress convolutional layers using C actions and another one that compresses dense layers using D actions. If there are two conv layers and 3 dense layers, 5 actions have to be selected in a sequence using both agents in order to fully compress the model. I read the paper AdaDeep and found it really useful for my research, but I don't get why the authors select all actions and they only calculate the reward after completely compressing the network instead of getting the reward after each action. In their place I would select the action, calculate the reward of that action and store it in the replay. By only using immediate reward, the agent should be able to learn which sequence of actions would work the best for the current model. Why assign the same reward to the selected actions for each layer? Is it only because the outcome was due to the combination of actions and they want to speed up training? If my understanding is correct, assigning the immediate reward to each action would yield the same results in the long run. Thanks in advance. submitted by /u/ElvishChampion [link] [comments]  ( 1 min )

After reading D, Multi's post, I'm wondering is it possible that two different agents to take action in their own action space & their own frequency? submitted by /u/YMXin1999 [link] [comments]

The environment makes a lot of sense to me, as a human. It’s a network composed of nodes and edges. Nodes are the points in the network, and edges are the lines connecting them. The entire thing resembles a real street network and uses coordinate points to position itself on a graph. Of the entire map, select nodes will represent gas stations, and other select nodes will represent stop locations for deliveries. The agent will start at a node and map a route to each location, essentially by stringing together an array of connected edges. It’ll also need to travel to gas stations every X miles or it’ll run out of gas. Now, I’ve never done this before so I’m gonna bounce some of my ideas off a wall here Passing the entire thing to an agent and having it render a graph and whatnot to determ…  ( 2 min )

In this repository that I'm looking at, there's an input variable whose meaning I don't understand. At line 135: https://github.com/google-research/google-research/blob/722494ce68130a7409bf94500002c79014905d53/social_rl/multiagent_tfagents/multiagent_ppo.py#L135 train_step_counter: An optional counter to increment every time the train op is run. Defaults to the global_step. What is train_step_counter? submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

hey guys I guess this can be trivial for the most of you but I can't get around it, how do I prove this is an exponential weighted average. Thanks The equation is labeled (10) in the link https://chowdera.com/2021/12/202112200806190809.html from reinforcement learning: an introduction by Sutton and barto , tracking a non stationary problem in chapter 2 submitted by /u/ma7modbasha [link] [comments]  ( 1 min )

With the internet producing quintillions of readily available information per day, you could be forgiven to think that data is losing its value. Apparently, data is one of those weird commodities that go up in value the more they are available, or perhaps we haven’t produced enough to attain the demand-supply equilibrium. Virtually all companies… Read More »Data quality: What and why is it important? The post Data quality: What and why is it important? appeared first on Data Science Central.  ( 3 min )

Data scientists face a problem: machine learning models need to be trained on labeled datasets, but labeling the data is tedious and time-consuming. Enter automatic data labeling, in which most of the preprocessing work is done by a computer.  At first glance, automatic data labeling sounds too good to be true. Of course, more automation… Read More »2 Ways in Which Automatic Data Labeling Saves Time and Costs The post 2 Ways in Which Automatic Data Labeling Saves Time and Costs appeared first on Data Science Central.  ( 4 min )

Integrated Marketing Communications (IMC) is an effective communication process that is intended to strengthen the relationship between the customer and the company while enhancing the company’s sales. IMC utilizes a combination of traditional and new approaches in marketing. IMC uses the channel that is most effective to reach the customer. This blog will look at… Read More »4 Successful Integrated Marketing Communications Examples The post 4 Successful Integrated Marketing Communications Examples appeared first on Data Science Central.  ( 4 min )

A new book predicts artificial intelligence will soon replace astronauts. The authors posit that robots are cheaper, more reliable, and better suited to space travel. But with the human desire for exploration, AI is unlikely to replace astronauts fully. AI will close the gap with human capabilities in the next few decades and surpass them… Read More »No, AI won’t replace astronauts – and here’s why The post No, AI won’t replace astronauts – and here’s why appeared first on Data Science Central.  ( 4 min )

The implementation of digital technologies blurs the line between the physical and digital world. It has become clear that there is a strong need for digital transformation to achieve the next level of efficiency, connectivity, and flexibility needed in manufacturing to weather modern-day disruptions, risks, and fluctuating demands. 5G SMART believes that 5G will be… Read More »Smart Factory- Building Future with 5G The post Smart Factory- Building Future with 5G appeared first on Data Science Central.  ( 2 min )

Stakeholder Journey Maps are a fabulous tool to intimately understand what a stakeholder is trying to accomplish (their objectives and intentions) and the steps/actions/decisions that stakeholder needs to make to complete their journey. Stakeholder Journey Maps are commonly used to help designers to create the optimal user interface and nicely segue into UI storyboards and… Read More »Using Stakeholder Journey Maps to Re-invent, not Just Optimize, Your Business Processes The post Using Stakeholder Journey Maps to Re-invent, not Just Optimize, Your Business Processes appeared first on Data Science Central.  ( 5 min )

Background Digital Twins are virtual representations of physical objects, and they can be connected with their physical counterparts. Through this connection, Digital Twins contribute to the convergence of the real and the virtual world. While the Digital twin’s concept is focused on the manufacturing industry, the paper “Dimensions of Digital Twin Applications – A Literature… Read More »An analysis of Digital Twin Applications across industries The post An analysis of Digital Twin Applications across industries appeared first on Data Science Central.  ( 6 min )

Data science is a wide field with many specializations, and an individual can have a great career with a data science degree. However, curriculums vary between schools, and the specific data science classes taught in one school may not be taught in another. There are several core skills in the data science field that recruiters… Read More »Make Sure Your Online Data Science Courses Teach These 6 Core Skills The post Make Sure Your Online Data Science Courses Teach These 6 Core Skills appeared first on Data Science Central.  ( 3 min )

I help lead a working group focused on personal knowledge graphs (PKGs). Lately, it’s functioned as a discussion and demo evaluation group for new technologies and how they might be used in a knowledge graph context.   Different individuals want to annotate different kinds of data. Some do a lot of research. For them, the need is… Read More »What Personal Knowledge Graphs Have to Do with Business The post What Personal Knowledge Graphs Have to Do with Business appeared first on Data Science Central.  ( 4 min )

The world of healthcare has consistently evolved, yes, but the fact remains it has gone through tremendous change ever since the coronavirus pandemic started, thus driving the need for modern solutions to meet the increasingly varying needs of patients. In this context, mobile apps have proven to be the leading tool that has driven focus… Read More »Healthcare App Development: Why You Should Opt for React The post Healthcare App Development: Why You Should Opt for React appeared first on Data Science Central.  ( 3 min )

Agile, Agile 2 and Agility, Part III In the previous articles in this series, we discussed the role that agile digital delivery capabilities plays in your company’s competitiveness and why rapid delivery is so important.  This article will look at the many reasons that Agile adoptions frequently fail to deliver what companies expect and suggest… Read More »Why Agile Often Fails and What to Do When It Happens The post Why Agile Often Fails and What to Do When It Happens appeared first on Data Science Central.  ( 7 min )

When it comes to protecting your computer, you can do a few basic things. First, create separate user accounts for work and personal data. Make sure to back up your data and use a firewall. Also, make sure to encrypt it.You should make sure to back up any important documents or photos you may have… Read More »How to Protect Your Computer Data The post How to Protect Your Computer Data appeared first on Data Science Central.  ( 4 min )

Yes and no. So, if you asked this question, good one! When I was new to this stuff, I had the same question and searched up a lot about it.  ( 3 min )

This week In the NVIDIA Studio, we’re launching the April NVIDIA Studio Driver with optimizations for the most popular 3D apps, including Unreal Engine 5, Cinema4D and Chaos Vantage. The driver also supports new NVIDIA Omniverse Connectors from Blender and Redshift. The post In the NVIDIA Studio: April Driver Launches Alongside New NVIDIA Studio Laptops and Featured 3D Artist appeared first on NVIDIA Blog.  ( 5 min )

A new artificial intelligence technique only proposes candidate molecules that can actually be produced in a lab.  ( 6 min )

A Blaschke product is a function that is the product of Blaschke factors, functions of the form b(z; a) = |a|  (a – z) / a (1 – a*z) where the complex number a lies inside the unit circle and a* is the complex conjugate of a. I wanted to plot Blaschke products with random […] Random Blaschke products and Mathematica binding first appeared on John D. Cook.  ( 2 min )

I'm not going to make this vague. Specifically I'm seeing a lot of comments related to Elon Musk saying he's going to remove bots from Twitter. There's a lot of speculation on how this could be done with comments suggesting a captcha-based system for every post/reply (or maybe every action?). More specifically people seem fixated on captcha systems that can't be botted. (Ignore for a moment the accessibility issues and audio fallbacks that might be required). I'm aware that Tesla transitioned to using more massive synthetic datasets for training, so this might be somewhat outdated. That said they do have a lot of data collection of new real world data from their sensors. This has me curious on the estimated value of large-scale captcha systems directly tied with a company that might need large-scale labeling services. I'm sure researchers here have run numbers on labeling services and how to best to utilize them. (Goes without saying the prices vary quite a bit and cover a wide range of tasks from simple bounding boxes to relatively expensive polygons for semantic labeling. For example just as a reference: https://cloud.google.com/ai-platform/data-labeling/pricing ). Most services don't list prices for like 500 million tasks which makes sense given that's a lot. A captcha system would have independent users redoing tasks multiple times to find baselines, but in general it would average to find a ground truth label. This redundant work isn't wasted in this sense as it can find say difficult scenarios and refine labels. I could naively say 500 million / (10 USD/1000 tasks) = 5 million USD a day not counting server costs and development. Not super scientific though and it seems high. (I have doubts on if gathering 500 million samples of value a day to even get labeled is realistic). I digress, what would you say is the hypothetical value of such a system using short captcha tasks? submitted by /u/Sirisian [link] [comments]  ( 2 min )

AI21 Labs’ Modular Reasoning, Knowledge and Language (MRKL, pronounced “miracle”) system – and Jurassic-X includes one or more language models, and augment them with external knowledge sources as well as symbolic reasoning experts that can handle tasks that lie beyond the reach of neural models. There are 55 different task-specific modules that MRKL currently supports. If the router is unsure which module is best, it calls on Jurassic-1. Jurassic also helps compose the contextual language around MRKL’s response. This allows MRKL to give factual answers with up-to-date information instead of being limited to its training data alone, and gives it the ability to carry out a much wider range of NLP tasks as compared to other LLM's like Google's PaLM or OpenAI's GPT-3 AI21 blog and whitepaper here Video here submitted by /u/SlightSituation [link] [comments]  ( 1 min )

I'm working on an Edge ML product where we train models in the cloud and then run them on a device using tensorRT. We're considering switching to using the Nvidia TAO Toolkit for training. If you've used TAO, do you like it? Is it limiting? Our alternative is training in pytorch and then converting to ONNX and then tensorRT separately. Thanks! submitted by /u/linguistBot [link] [comments]  ( 1 min )

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https://youtu.be/povBDxUn1VQ Automatic curriculum generation is one of the most promising avenues for Reinforcement Learning today. Multiple approaches have been proposed, each with their own set of advantages and drawbacks. This paper presents ACCEL, which takes the next step into the direction of constructing curricula for multi-capable agents. ACCEL combines the adversarial adaptiveness of regret-based sampling methods with the capabilities of level-editing, usually found in Evolutionary Methods. ​ OUTLINE: 0:00 - Intro & Demonstration 3:50 - Paper overview 5:20 - The ACCEL algorithm 15:25 - Looking at the pseudocode 23:10 - Approximating regret 33:45 - Experimental results 40:00 - Discussion & Comments ​ Website: https://accelagent.github.io Paper: https://arxiv.org/abs/2203.01302 submitted by /u/ykilcher [link] [comments]  ( 1 min )

Hello hivemind. Context I am working on streamlined design strategies for (manual) neural architecture design. I recently came across a simple, receptive field-based strategy that allows me to reliably improve the architectures of some SOTA models like EfficientNet by up +1.5% top1-accuracy.That being said, there seems to be a trade-off between performance and number of computations I put into the same number of parameters.Basically, the more computationally expensive the architectural change is, the better the performance turns out to be. The number of parameters does not change in the process. The model becomes therefore more parameter efficient but computationally less efficient, which you could consider a somewhat Pyrrhic victory. Now to my question: Is there use for parameter efficiency in models when it does not also coincide with computational efficiency? Is there any literature on the topic you can recommend? submitted by /u/KrakenInAJar [link] [comments]  ( 1 min )

So far I've only seen feature importance calculated on one model/ training set at a time, However, with cross validation there are many models built on different training sets, how do you calculate feature importance in this case? Just do it on one model? Or calculate feature importance for each model and somehow aggregate it (e.g., by averaging)? submitted by /u/Comprehensive-Egg707 [link] [comments]  ( 2 min )

Hello, I wrote the blog post about text-conditional image generation using diffusion models (including DALLE-2). Let me know what you think! ​ https://sangyun884.github.io/recent-trends-in-diffusion-based-text-conditional/ submitted by /u/Impressive-Mirror430 [link] [comments]

Hey r/MachineLearning, Nir from DagsHub here, and I'm thrilled to share a project we're launching today that will hopefully unlock GitHub Actions for ML. GitHub Actions solved a DevOps burden many of us felt by providing an easy-to-configure CI/CD tool to build, test, and deploy pipelines. However, when it comes to ML pipelines, and working with data, models, and experimentation in mind, the workflow is not as well defined and can get tricky to implement. DagsHub is kind of like GitHub for machine learning, which extends what GitHub did for code management to track data, models, experiments, and data pipelines. We do this by integrating awesome open source tools like DVC, MLflow, Label Studio, and more. One of our most requested features was a deeper integration with GitHub that will let…  ( 1 min )

Hi folks, our ICLR 2022 Blog post on "The 37 Implementation Details of Proximal Policy Optimization" is live 😀 Our post makes it easier to understand the nitty-gritty PPO's implementations with 1) 🎥 video tutorials 2) 📜 detailed references and explanations 3) ⌨️ really simple code Here are the links: Official URL: https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/ Twitter thread: https://twitter.com/vwxyzjn/status/1518589115163369472 OpenReview link:https://openreview.net/forum?id=Hl6jCqIp2j GitHub repo: https://github.com/vwxyzjn/ppo-implementation-details YouTube tutorial on PPO: https://www.youtube.com/playlist?list=PLD80i8An1OEHhcxclwq8jOMam0m0M9dQ_ I am the main author & feel free to ask me anything here. submitted by /u/vwxyzjn [link] [comments]  ( 1 min )

Hey, 🤗 Hugging Face is offering a workshop (June 6) for instructors of machine learning and data science who would like to learn how the open-source ecosystem can be used in their classes. After this workshop, you will know how to: 🧑‍💻 Teach Transformers models & famous ML libraries 🤖 Onboard students to the Hub to build/host projects 💾 Publish models/datasets in a few lines of code During the workshop, you will be invited to join the following page for a better understanding of our open-source solutions: https://huggingface.co/teach For more details about the workshop content, visit: https://hf.co/teaching Feel free to register here:) submitted by /u/VioletteLep [link] [comments]  ( 1 min )

It could be pictures of anything, except of humans. But it would be better if it were not focused on a single topic like dog images. submitted by /u/TheManveru [link] [comments]  ( 1 min )

TL;DR: Download dataset of labelled Web pages, WebTraversalLibrary for scripting web interactions Hi everyone! Our group at Klarna has been putting in a ton of work into deep learning for the Web over the past few years and we've made a couple of useful resources available for the research community. You might find them interesting if you're looking for new ideas for spare-time or even post-grad research projects. We've open-sourced a dataset of about 50k labeled product web pages from roughly 8000 distinct e-commerce merchants, available as MHTML and WebTraversalLibrary clones (see next point :) ), along with the corresponding screenshots. Not all of the MHTMLs render correctly, but the ones that do also have screenshots in a corresponding dataset for CV applications. You can find doc…  ( 2 min )

Just like Eleuther did with GPT3? submitted by /u/invertedpassion [link] [comments]  ( 2 min )

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Does anyone know where chatbots specifically the ones on Chai get their information? This one knew what meta-analysis was and knew who the main character of a show was I do not believe the bots are real people based on both the information I've seen and my experience with them glitching out Plus occam's razor yk Im guessing they can use google or something but it's odd because they clearly don't have access to the time submitted by /u/Shadowfax42- [link] [comments]  ( 1 min )

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(I have no experience with photoshop) submitted by /u/xXNOdrugsForMEXx [link] [comments]

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is there a way to access any of openAI APIs if it's not avalable in my country? submitted by /u/dogaryy [link] [comments]  ( 1 min )

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Hi, I am having some trouble with a LSTM problem regarding walk forward validation in my LSTM. The problem is described in this stackoverflow post: https://stackoverflow.com/questions/71990833/using-predictions-instead-of-observed-values-in-walk-forward-validation-in-lstm If any one could help me that would be much appreciated submitted by /u/magnussendjoko [link] [comments]  ( 1 min )

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The last few years have seen rapid growth in the field of natural language processing (NLP) using transformer deep learning architectures. With its Transformers open-source library and machine learning (ML) platform, Hugging Face makes transfer learning and the latest transformer models accessible to the global AI community. This can reduce the time needed for data […]  ( 8 min )

Nordic Aviation Capital (NAC) is the industry’s leading regional aircraft lessor, serving almost 70 airlines in approximately 45 countries worldwide. In 2021, NAC turned to AWS to help it use artificial intelligence (AI) to further improve its leasing operations and reduce its reliance on manual labor. With Amazon Rekognition Custom Labels, NAC built an AI […]  ( 5 min )

MIT researchers can now estimate how much information data are likely to contain, in a more accurate and scalable way than previous methods.  ( 6 min )

Researchers have developed a technique that enables a robot to learn a new pick-and-place task with only a handful of human demonstrations.  ( 7 min )

SimpleGrid is a simple gridworld environment for OpenAI gym. It is easy to use and customise and it is intended to offer an environment for quick testing and prototyping different RL algorithms. I developed this environment by taking inspiration from the FrozenLake environment and gym-minigrid. Check it out at: https://github.com/damat-le/gym-simplegrid ​ https://i.redd.it/prqd7muujqv81.gif submitted by /u/damat-le [link] [comments]  ( 1 min )

Hi, some newer CPU's (Apple M1, Intel Alder Lake) have started using a hybrid CPU topology. I.e. the CPU consists of some P-cores (high performance cores) and E-cores (slower 'eco' cores). I personally do not own such a CPU yet but I'm considering upgrading to one soon, so I am looking for experiences of people with such a CPU on training reinforcement models. Does everything work as expected? Are there annoyances? I'm using Ray Tune + RLlib in which I assign a single core per trial. In this case I'm expecting that trials running on E-cores will simply run (much) slower than those running on P-cores. In case a single trial gets assigned both a P-core and an E-core, I do expect a serious slow-down. These are all guesses really, so I'm looking for people with actual experience here. Thanks. submitted by /u/katsu9 [link] [comments]  ( 1 min )

Hey everyone, I managed to implement the policy iteration from Sutton & Barto, 2018 on the FrozenLake-v1 and wanted to do the same now Taxi-v3 environment. My code has been running now for 45min so I guess there is something wrong, but I can't wrap my head around what it could be. Would appreciate some input on what I need to change so that it will work. Please see my code here: ```[python] import gym # openAi gym import torch import matplotlib.pyplot as plt from tqdm import trange # progressbar torch.manual_seed(4) env = gym.make('Taxi-v3') def policy_evaluation(env: gym.Env, policy: torch.Tensor, gamma: float, threshold: float): V = torch.zeros(env.observation_space.n) delta = float("inf") while delta >= threshold: V_tmp = torch.empty(env.observation_space.n) for state in range(…  ( 1 min )

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Hey there! We're happy to announce the launch of the Hugging Face Deep Reinforcement Learning class! 🤗 👉 Register here https://forms.gle/oXAeRgLW4qZvUZeu9 In this free course, you will: 📖 Study Deep Reinforcement Learning in theory and practice. 🧑‍💻 Learn to use famous Deep RL libraries such as Stable Baselines3, RL Baselines3 Zoo, and RLlib. 🤖 Train agents in unique environments with SnowballFight, Huggy the Doggo 🐶, and classical ones such as Space Invaders and PyBullet. 💾 Publish your trained agents in one line of code to the Hub. But also download powerful agents from the community. 🏆 Participate in challenges where you will evaluate your agents against other teams. 🖌️🎨 Learn to share your environments made with Unity and Godot. 👉 Register here https://forms.gle/oXAeRgLW4qZvUZeu9 📚 The syllabus: https://github.com/huggingface/deep-rl-class https://preview.redd.it/b409a9sscov81.jpg?width=1920&format=pjpg&auto=webp&s=ebdfa10c220b5a3dec17894bc0f955ed9d8f7634 If you have questions and feedback, I would love to answer them, Thanks, submitted by /u/cranthir_ [link] [comments]  ( 1 min )

Hi, I'm wondering if I could train the state-based locomotion well from the scratch. In my case, I trained SAC agent and It showed divergence of the q function. I think the cause is the unscaled vector input. Before the trouble shooting, I want to ask someone trained agent in this environment. Thank you for reading. https://github.com/deepmind/dm_control/tree/main/dm_control/locomotion submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 1 min )

I started off with OpenAI's mountain car, and I don't know where to even start. I got the setup working with the environment, but now have no idea how to train it. How do I learn to code for RL? Every tutorial I have seen so far has implemented Q Learning algorithms completely with little explanation. I looked at the solved code and it doesn't make sense to me. How should I prepare before I go into OpenAI? submitted by /u/TrepidationTD [link] [comments]  ( 1 min )

Posted by Cat Armato and Callan Hajosy, Program Managers The 10th International Conference on Learning Representations (ICLR 2022) kicks off this week, bringing together researchers, entrepreneurs, engineers and students alike to discuss and explore the rapidly advancing field of deep learning. Entirely virtual this year, ICLR 2022 offers conference and workshop tracks that present some of the latest research in deep learning and its applications to areas ranging from computer vision, speech recognition and text understanding to robotics, computational biology, and more. As a Platinum Sponsor of ICLR 2022 and Champion DEI Action Fund contributor, Google will have a robust presence with nearly 100 accepted publications and extensive participation on organizing committees and in workshops…  ( 12 min )

The previous post explained how to define a projective plane over a field F. Now let’s look at how we do geometry in a projective plane. Definitions We have a definition of points from the other post: a point is a triple (a, b, c) of elements of F, with not all elements equal to […] Projective duality first appeared on John D. Cook.  ( 3 min )

Given a field F, finite or infinite, you can construct a projective plane over F by starting with pairs of elements of F and adding “points at infinity,” one point for each direction. Motivation: Bézout’s theorem A few days ago I mentioned Bézout’s theorem as an example of a simple theorem that rests on complex […] Finite projective planes first appeared on John D. Cook.  ( 4 min )

Picture a person walking in a park by a pond. The surrounding environment contains a number of moving objects that change the quality of the environment: clouds moving to hide the sun, altering the quality of light; ducks gliding across the pond, causing its surface to ripple; people walking along a path, their images reflecting […] The post PPE: A fast and provably efficient RL algorithm for exogenous noise appeared first on Microsoft Research.  ( 9 min )

When brainstorming a scene to best showcase the groundbreaking capabilities of the Omniverse platform, some NVIDIA artists turned to a cherished memory: enjoying ramen together in a mom-and-pop shop down a side street in Tokyo. Simmering pots of noodles, steaming dumplings, buzzing kitchen appliances, warm ambient lighting and glistening black ledger stools. These were all Read article > The post Let Me Shoyu How It’s Done: Creating the NVIDIA Omniverse Ramen Shop appeared first on NVIDIA Blog.  ( 4 min )

A paper released today describes in the greatest detail to date the atmospheres on distant planets. Seeking the origins of what’s in and beyond the Milky Way, researchers surveyed 25 exoplanets, bodies that orbit stars far beyond our solar system. Specifically, they studied hot Jupiters, the largest and thus easiest to detect exoplanets, many sweltering Read article > The post Stellar Weather: Researchers Describe the Skies of Exoplanets appeared first on NVIDIA Blog.  ( 4 min )

At some point, we’ve all used Google translate, Microsoft,DeepL or Bing translator to impress our friends/colleagues who speak a different…  ( 4 min )

Figure 1: Summary of our recommendations for when a practitioner should BC and various imitation learning style methods, and when they should use offline RL approaches. Offline reinforcement learning allows learning policies from previously collected data, which has profound implications for applying RL in domains where running trial-and-error learning is impractical or dangerous, such as safety-critical settings like autonomous driving or medical treatment planning. In such scenarios, online exploration is simply too risky, but offline RL methods can learn effective policies from logged data collected by humans or heuristically designed controllers. Prior learning-based control methods have also approached learning from existing data as imitation learning: if the data is generally “goo…  ( 11 min )

Despite it's immense popularity in academia, it's surprisingly difficult to download the ImageNet Object Localization dataset. As far as I can tell this is due to legal issues -- no single entity owns the copyright to the images, so no entity can host the whole dataset. The result is that if you want to use ImageNet you're forced to either manually scrape a million URLs (requiring both cpu time, your time, and imposing costs on a million unsuspecting websites), know somebody who has already done that, or fetch it from a legally questionable source. So I have a couple questions: Is the owner of the ImageNet dataset on Kaggle performing a selfless public service, thanklessly accepting legal liability to make ImageNet more accessible? Or is she protected (e.g. by fair use)? Is she required to accept DMCA requests? If I'd like to share ImageNet (I've recently downloaded it and processed it to be ~10 GB, which seems like a helpful thing to share), is there any legally safe path for me to do this? submitted by /u/you-get-an-upvote [link] [comments]  ( 3 min )

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Previous weeks : 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 Week 1 Week 11 Week 21 Week 31 Week 41 Week 51 Week 61 Week 71 Week 81 Week 91 Week 101 Week 111 Week 121 Week 131 Week 2 Week 12 Week 22 Week 32 Week 42 Week 52 Week 62 Week 72 Week 82 Week 92 Week 102 Week 112 Week 122 Week 132 Week 3 Week 13 Week 23 Week 33 Week 43 Week 53 Week 63 Week 73 Week 83 Week 93 Week 103 Week 113 Week 123 Week 133 Week 4 Week 14 Week 24 Week 34 Week 44 Week 54 Week 64 Week 74 Week 84 Week 94 Week 104 Week 114 Week 124 Week 134 Week 5 Week 15 Week 25 Week 35 Week 45 Week 55 Week 65 Week 75 Week 85 Week 95 Week 105 Week 115 Week 125 Week 135 Week 6 Week 16 Week 26 Week 36 Week 46 Week 56 Week 66 Week 76 Week 86 Week 96 Week 106 Week 116 Week 126 Week 7 Week 17 Week 27 Week 37 Week 47 Week 57 Week 67 Week 77 Week 87 Week 97 Week 107 Week 117 Week 127 Week 8 Week 18 Week 28 Week 38 Week 48 Week 58 Week 68 Week 78 Week 88 Week 98 Week 108 Week 118 Week 128 Week 9 Week 19 Week 29 Week 39 Week 49 Week 59 Week 69 Week 79 Week 89 Week 99 Week 109 Week 119 Week 129 Week 10 Week 20 Week 30 Week 40 Week 50 Week 60 Week 70 Week 80 Week 90 Week 100 Week 110 Week 120 Week 130 Most upvoted papers two weeks ago: /u/CatalyzeX_code_bot: Paper link /u/lauren_v2: paper Besides that, there are no rules, have fun. submitted by /u/ML_WAYR_bot [link] [comments]  ( 1 min )

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Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 2 min )

Hi, seemingly it's become a staple in conv net inner-working visualization to put a network in eval mode, sample a random noise image, and optimize the image in relation to the activation of some internal neuron. From what I saw, most examples of this on the internet are using an Adam optimizer for this with a learning rate of 0.1 and a weight decay of 1e-6. This doesn't seem quite right with me, So if any of you know what's the source for this convention and if there are other alternatives I'd appreciate this information very much. Thanks! submitted by /u/ondrea_luciduma [link] [comments]  ( 1 min )

I have some experience with ML but not at all with RL. I know basic theory and want to just get started with the programming part. I saw OpenAI's gym, but I want to learn RL that can be applied anywhere. I don't want to be specifically constrained to OpenAI's gym and want something where I can apply it to game development, such as Unity. Are there any good resources to just do an RL project? submitted by /u/TrepidationTD [link] [comments]  ( 1 min )

I have a few questions about implementing the N Step version of Prioritized Replay Buffer (for Rainbow DQN). I'm implementing the Atari version of this buffer. To conserve memory, I'm only storing the states (and the last state of each episode) in an unstacked manner. That is, if the frame stack is 4, and the shape of states returned by the environment is 4, I'm storing only the last state of the stacked states. This way the buffer contains all transitions from each episode. As for the N Steps part, I'm only calculating the n_step states when getting states from the buffer instead of storing the N Step transitions directly. For the prioritized version, how do the priorities work? If I wasn't trying to conserve memory, I would have stored the N Step stacked states directly and update the priorities for the segment trees and the segment tree pointers only when moving the data from the N Step buffer to the main buffer. But now that I'm calculating the N Step experience directly when sampling and not when adding data to the buffer, when do I update the priorities and the tree pointer? Once per state? Once per stacked state? Once per N Step stacked state? When I update the priorities for the sampled batch, the priorities are associated with multiple states (because the states are stacked). But because I don't store the stacked states and only the raw unstacked states, to which of these states should I update the priority for? And because I don't store the n step transitions anymore, to which of the N Steps should the priorities be associated with? If I want to create such a buffer for a vector env, how would I go about doing it? I'm thinking of maintaining a separate segment tree for each env. Is that correct? Is there a better way? submitted by /u/SirRantcelot [link] [comments]  ( 1 min )

I don't understand if having a centralized critic in multi-agent RL is the same as having a centralized training decentralized execution approach. Can you help me clarify this? submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

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Consider a fixed maze with size (100x400) and fixed start and endpoints (1,1) and (99,399), where doors dynamically appear around the agent every time an observation has been made. The doors disapear after some time step n (for simplicity say n=1). For pitty sake lets say the optimum path length is 1000 steps and there is only one way to reach it. I have two questions: (1) What would be the most appropriate way to frame reward function? I have tested both volatile aproach (i.e. episodeis terminated if agent steps into wall or previosuly made path, otherwise is rewarded in "cheese" every 5 steps) and not-so-volatile approach (i.e. free roam for maximum 2000 steps with cheese reward every 5 steps). Evidently these do not work, are there other perhaps more promising approaches or such large mazes? (2) Does the colour of objects in a frame matter to the (convolutional) DQN's learning of the maze. Considering a 3 channel RGB input, is there perhaps a smarter way to colour code the walls/emptypath/etc.? Its a bit of a weird question but my curiosity arises from short-term memory examples (namely, space invaders) where the input, multiple greyscale frames, is chosen over one rgb frame - so to this degree channels can be understood as higher-level "features" of a frame, hence, can the colours of different objects be optimised? If so is there a logical way of viewing such optimisation? Any other advice is always welcome :) ​ N.B. yes, this could probably be solved with other easier methods but lets just say DQN (or other deep-Q alternatives) is needed. submitted by /u/Background-Cable-491 [link] [comments]  ( 2 min )

Hello, I aim to use RL in for classification problem, But I can't see where is the difference between using RL and other ML algorithms that are used in classification (such as MLP, KNN, SVM ..) since we have a train phase in which we teach an agent ( or ML algorithm) the classe of each sample of a labeled dataset. Ok the manner of teching is different but the concept is the same. Then, in a second phase, we test the model with a test set. My question is, if I choose RL for classification problem, what is the contribution that we can have compared to another algorithm? submitted by /u/fatenLouati [link] [comments]  ( 2 min )

Hi, I am trying to create a continuous 2d maze environment. I tried several algorithms but no one can give me a stable 1 success rate. From time to time, it gets stuck around the obstacle corner and fails to move anymore. Like the picture shows below. I guess it relates to my bad design for giving the next observation for an action that can't make the agent move forward. Currently, my design evenly separates the action into 10 substeps, and returns the observation which corresponds to the step right before the agent meets an obstacle. It looks like I won't have this issue for mujoco environment like Ant. But it is really hard to see their design. https://preview.redd.it/swohj8h8qev81.png?width=282&format=png&auto=webp&s=7353a62e99ff2141b3660c68ae34ce4f5cd9b94f submitted by /u/AnimatorRemarkable20 [link] [comments]  ( 1 min )

First of all, let's discuss what the level of AI is now. If the "level" refers to the capability of competing, the current AI has been very closed to the top human player in some types of games, like chess, Texas Poker, and Mahjong of CARDS, DOTA2 of MOBA, as well as StarCraft2 of RTS. As for other games, if we have enough human resources and computing performance, we also can get similar results. If the "level" has other meanings, like AI agents having human behavior, intelligent NPC can be designed specifically for different people so that they can have different gaming experience. These are all at the stage of issue-defining and exploring new technology solutions. Although traditional game AI is mostly based on hard code, it still has much prior knowledge. In recent years, some hot ML-r…  ( 4 min )

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Dear all, Currently I am writing my thesis on the effects of Artificial Intelligence (AI) on employee performance through employee engagement. If you work in a company that uses AI and you: - have a direct relationship (e.g. data scientist) or indirect relationship (e.g. business manager) with AI - often or sometimes use AI in your daily work then your insights are essential for my master thesis research and I would really appreciate it if you would fill out the survey below (5-10 min / English & Dutch translation). https://uva.fra1.qualtrics.com/jfe/form/SV_4TTnzJE4IQUoHWK Please feel free to spread the survey to as many relevant people you may know. Much appreciated!! Britt submitted by /u/BrittHermans [link] [comments]  ( 1 min )

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​ https://preview.redd.it/beo6czc6hhv81.png?width=1706&format=png&auto=webp&s=385f65ebfed9344781d1f9238d8d508dae27c0a3 In the last decade, AI research has brought astonishing results in many fields, and, undoubtedly, AI is nowadays a central technology in many aspects of our life. As new ideas are proposed every day, this continuous research usually comes with infinite applications: from the algorithms assisting surgeons in complex operations to the one which allows unlocking our phone using just our face. In this evolution from the idea to the actual implementation, it is often ignored how hard the passage between theoretical research and working application is. We can refer to this process as AI Development Cycle for Edge AI and can be divided into three phases related to 1) data, 2) model, and 3) evaluation. Many aspects must be considered: first, each different AI application requires a specific dataset. For this reason, in this step, the aim is to prepare the data, which, as is well known, is one of the crucial topics of AI: a good algorithm always relies on a good dataset. This phase can be divided into data collection, curation, labeling, and preparation. Continue reading submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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​ https://preview.redd.it/qaqevbqlfev81.png?width=1024&format=png&auto=webp&s=8c2759d29a3713c2ffb998e20e8995cee4d8b2b4 submitted by /u/Smek_dev [link] [comments]

In the last decade, AI research has brought astonishing results in many fields, and, undoubtedly, AI is nowadays a central technology in many aspects of our life. As new ideas are proposed every day, this continuous research usually comes with infinite applications: from the algorithms assisting surgeons in complex operations to the one which allows unlocking our phone using just our face. In this evolution from the idea to the actual implementation, it is often ignored how hard the passage between theoretical research and working application is. We can refer to this process as AI Development Cycle for Edge AI and can be divided into three phases related to 1) data, 2) model, and 3) evaluation. Many aspects must be considered: first, each different AI application requires a specific dataset. For this reason, in this step, the aim is to prepare the data, which, as is well known, is one of the crucial topics of AI: a good algorithm always relies on a good dataset. This phase can be divided into data collection, curation, labeling, and preparation. Continue reading submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

This is about a project that I am working on; Hope the ML community can help me! I have collected few hours of air pollutants data using Aeroqual sensors and custom made sensors. 3 types of data is available in the project; aeroqual, custom, council data. Where council data can be taken for granted (It comes from the govt installed high spec sensor). Aeroqual is a commercial sensor manufacturing company, its data should be accurate. The first part of the project is about checking the accuracy of custom sensor. So, I have done few analysis on the data; and found that custom sensor data has similarity (but not same, there are so much variation in the custom sensor data) with council sensor data but aeroqual data is way different. I am attaching the plot below which I have done. ​ So I need to know is there any method that I can find relationship between these three datasets? Is it possible to make these data align togather? I need to build an ML model to predict the air pollutant level using this data. any tips for getting this thing working? - Thanks in advance ​ https://preview.redd.it/fzu7dbsz0dv81.png?width=885&format=png&auto=webp&s=516d65fe3290ac8a28159547880f9dc972922b64 submitted by /u/Codename_17 [link] [comments]  ( 1 min )

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My problem is probably what you have guessed: it's understanding the technical specifications which are usually written in a non-coding-friendly way. Sometimes crucial information is completely missing from the paper ex: loss function description for a DL algorithm. For the lucky cases where there are already available implementations on github to a given paper, usually they are either very distinguishable from each other in terms of code structure which questions their validity or whether they match what the paper authors intended specially with varying measurable results, or they are almost exact copies from one another. There are numerous examples where I can show specific papers with varying degrees of complexity, and discuss why the conversion can be tricky but they may require standalone discussions themselves, likely outside the scope of this one. Is there a way to approach the problem assuming the absence of reference code? submitted by /u/shine-box [link] [comments]  ( 2 min )

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I host an important org level meeting (~100 attendees) every week, and need to share minutes after the meeting. I am so tired of listening to conversations again just to capture important points, summarise discussion and action items. Is there any model/api which can help me do that? I use Amazon transcribe to generate transcripts, which helps, but it is not very accurate. For me the priority would be: 1) Model/api which is better than Amazon transcribe 2) Auto Identify speakers / speaker diarization (since mostly the same set of people speak) 3) Summarise the conversations into topics (we have time and agenda based discussion) I am sure this might be a problem across the industry since most of the meetings happen online, and someone wastes hours after meeting to send notes. I did find some tools which summarise the transcript, but i need to auto send in a specific format and identify topics based on conversion (maybe we can input the agenda in advance). Also this is private information, so I need something on premise, hence looking for a repo or model which i can use to build something on top. Please let me know if something exists or someone working on similar projects. Happy to collaborate and contribute. submitted by /u/super_commando-dhruv [link] [comments]  ( 1 min )

This research will test the human ability to distinguish human written text from text generated by artificial intelligence. Participating will only take 10 minutes. You will receive 2 short news articles about the same topic. One will be written by a human, the other one will be generated by artificial intelligence. It is up to you to find out which one is written by artificial intelligence. You will be asked to do this for four different subjects, namely: Science, Economics & Politics, Society and Sports. At the end of the survey you will receive feedback on how well you have performed. The human written articles were collected from various news websites. The Articles created by artificial intelligence were generated using GPT-3 from OpenAI. Purpose of the research: We are trying to find out how well GPT-3 performs across subjects. Are there any subject GPT-3 is better at writing about, or is he equally good across all subjects. Secondly we are testing the ability of GPT-3 to generate articles about events that happened after the training of the model. You can participate by clicking on the link below, thank you very much for your participation. https://vub.fra1.qualtrics.com/jfe/form/SV_b2E9f6hGxNDH13M submitted by /u/RobinSandersVUB [link] [comments]  ( 1 min )

2000 GPUs and 8000 CPUs. And where could I even get such a vast affordance? submitted by /u/samlerman [link] [comments]  ( 2 min )

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I selected 5 ready-made algorithms for face detection and compared them with each other by such metrics as Precision, Recall, IOU and time on the dataset I marked up. I am ready to accept your Pull Request with your solutions(algorithms) and results! GitHub: https://github.com/wb-08/face-detection-algorithms-comparison Blog post: https://habr.com/ru/post/661671/ submitted by /u/wb-08 [link] [comments]

I have been interviewing for a machine learning lead position. I have successfully passed 3 interview rounds (coding , HR, system design). I have my final interview with the VP of Engineering. When asked how best to prepare myself, they said they would like to test my ability to write "production quality" code in python. While I do have some experience, the downside is I worked in small R&D teams for a long time. Though I am knowledgeable in python, perhaps, I might have not followed all the industry best practices. If you are a hiring manager or interviewer, how would you test this ability? How do I prepare myself to prove my ability to write production grade code? Thank you all so much in advance. submitted by /u/mbkv [link] [comments]  ( 4 min )

I'm comparing different GAN models (CGan, DCGan, WGan, StyleGan) in tensorflow2. In general, I want to use the images that I generate with the generator to train a classifier while being as realistic as possible. At first, I wanted to let them train for 24 hours each, define some early stopping criteria and save the checkpoints with the lowest loss through a callback. But it seems that the lower loss does not always lead to more realistic images. So how do I compare the different models in a scientific way? Because the results highly depend on the epoch I choose and my subjective feeling, which images look the best. submitted by /u/Bonkikong [link] [comments]  ( 1 min )

https://www.youtube.com/watch?v=2E_6ARbrMmc submitted by /u/Thenamessd [link] [comments]

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Github: https://github.com/labmlai/neox Annotated implementation: https://lit.labml.ai/github/labmlai/neox/tree/main/src/neox/__init__.py Original repo from EleutherAI: https://github.com/EleutherAI/gpt-neox We have included samples showing how to generate text and to fine-tune. We haven't included a bunch of optimizations that were present in original GPT-NeoX to keep things simple. submitted by /u/hnipun [link] [comments]  ( 1 min )

Hello! If you are using jax and you need to pass some pytrees between processes, I may have something for you :) I developed a "treequeue". It is a queue that is made for pytree's nested arrays. The transfer speed is up to 10 times higher than regular queues. This is done by utilizing shared memory arrays and avoiding pickling data. This can be very useful when developing distributed architecture, e.g. distributed reinforcement learning where speed is at the upmost importance. In my case this implementation was very useful to remove bottlenecks when implementing PBT algorithms! https://github.com/thomashirtz/treequeues Cheers! submitted by /u/krenast [link] [comments]  ( 1 min )

Comes with ‘white paper’ and example notebooks… seems legit..? Anyone tried this out yet? Github Paper] submitted by /u/proportional-hazard [link] [comments]

I know that many people (including me) were surprised after seeing the image quality of ViT-VQGAN and disappointed to know there won't be no source code released. Therefore, I've decided to implement it by myself and here is the code. I hope this can help everyone as a starting point for ViT-VQGAN. submitted by /u/ThunaClone [link] [comments]

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In reviewing various approaches to end-to-end deep learning for autonomous driving, I've come across an interesting approach in this paper that I would like to discuss with others... I will begin by summarizing the approach: ​ A ResNet50 architecture is used as an encoder network with the input being an RGB image + depth map concatenated as (224 x 224 x 4). In the paper it is argued that a point cloud can also be used, or some other sensor modality would also work The encoder network output (feature map of 7 x 7 x 2048) is fed into a decoder network that takes it back to (224 x 224 x 5) with pixel wise semantic segmentation of 5 classes: lane, road line, sidewalk, vehicles or pedestrians, and others That same encoder output (feature map of 7 x 7 x 2048) is global average pooled to 2…  ( 2 min )

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Dall-E-2. Excellent. It's very high quality. But it's a combination of the data. ​ What did we want? We wanted some amazing work that made us cry with just one line of writing or one image. ​ "Oh, copied it well. It's pretty much the same." It's not enough. But how can that be improve? I think the answer is the feedback method. ​ ​ The current evaluation method of writing, image, video, and sound is too indirect. ​ Sales revenue Number of Subscribers Number of views Like / Dislike Ratings by section, Revisit Rate <<< Those are better than others Emotion analysis of Comments using AI Internal staff scores ​ There are so many conditions other than the quality of contents that people's judgment can intervene in. In the first place, people don't express exactly what they…  ( 3 min )

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Hello there, if one would want to get into AI and especially human like AI, would you still recommend getting into machine learning first? As far as i know machine learning doesnt even try to develop "human like" AI/"bottom up AI", but rather focuses on training algorythms to solve specific problems. I know human like AI is something thats highly complex and we still need years if not even decades to achieve something even close to it but i would appreciate tips and ideas nonetheless. (after reading through my question again this sounds like a generic question thats being asked here everyday, if thats the case please send me a link to a similar post if there is one :) ) submitted by /u/Garic152 [link] [comments]  ( 1 min )

Hi everyone Im doing a project with my friends where we should use computer vision/iot to create a solution for people with disabilities or in the healthcare system Any ideas please submitted by /u/armyy__ [link] [comments]

From improving the user experience to making the computational infrastructure more effective, AI is a crucial aspect of making current software systems and products perform as well as possible. AI is often more effective than even precisely developed human-crafted heuristic tactics today, whether it’s reducing latency, boosting the quality of a video stream, or streamlining the interfaces to match a specific person’s demands. But, to use AI more effectively in various products, several challenges must be addressed: the system must accommodate software engineers without machine learning backgrounds; it must provide mechanisms to optimize for a variety of product goals, which may differ from closed-form machine learning loss functions; it must distinguish causal connections from data correlations; and it must scale efficiently to train, host, and monitor vast numbers of AI models. Meta Researchers Develop ‘Looper,’ an end-to-end AI platform that has been designed with easy-to-use APIs for optimization, personalization, and feedback collecting to answer these needs. Looper may be used to support the entire machine learning lifecycle, from model training to deployment and inference to product evaluation and optimization. Looper allows us to modify the existing products to leverage AI for personalized optimizations rather than having to rebuild them around AI models. Currently, the Looper platform hosts 700 AI models and produces 4 million AI outputs every second. Continue reading Paper: https://arxiv.org/pdf/2110.07554.pdf submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

I'm looking to create a way to automate original story ideas based on previous ideas. I want to be able to input 1000+ original sentences and have an output of an original sentence that is inspired the previous ones. Are there any programs that can do this or will I need to develop my own? submitted by /u/yea_okay_dude [link] [comments]  ( 1 min )

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I am training PPO2 model on stable-baseline library. I have tabular data with 15000 rows, thus length of the episodes is 15000. I am using nminibatches=4, n_envs=1. For example, I have set total_timesteps=10000. During the training process agent will see 15000 rows several times and updates actions for each rows, but in some particular point, the rest of the time total_timesteps will not be enough to see the full episode, and only part of episodes is available in the last step of learning. To be concrete. For simplicity, lets say we have 10 raws, 23 total_timesteps. The agent will see the full episode 2 times, and only the first 3 rows in the third times and rest of the 7 raws have not seen during last step. I want to stop the learning process when Agent reaches the last time full episodes (above example stop learning at when total_timesteps=20) or define total_timesteps in such a way to see full episodes at the end of the training step. submitted by /u/Mariam_Dundua [link] [comments]  ( 1 min )

Hello guys, I am pretty new to the rl field and write now i am doing my thesis in it. I've come across a problem in my code. I created a custom environment and when i am trying to solve it with my dqn agent using stable baselines3, I am able to execute the code and print out the required things but the agent is not learning. Any help ? thanks. submitted by /u/last_2_brain_cells97 [link] [comments]  ( 1 min )

Hi guys, I am new to RL and reading tutorial of spinning up which focus on policy based algorithms. In the derivation of VPG, the tutorial said"The environment has no dependence on /theta(the parameter of policy), so gradients of R(/tau)(total return of the trajectory) with respect of /theta is 0. However, the trajectory depends on our policy, and our policy depends on /theta. As a result, I am confused why total return of trajectory is independent from /theta. submitted by /u/SkyRimT [link] [comments]  ( 2 min )

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I see lots of hardware oriented toward AI/ML stuff these days, including chips with hardware acceleration for neural networks. I'm thinking about how GPUs were initially designed for graphics calculations, but then things like CUDA and OpenCL were developed to make that hardware usable for broader applications of parallel processing. Are there any other things that you can do with a neural network besides backpropagation, that wouldn't be easier to do in other ways? submitted by /u/Bananawamajama [link] [comments]  ( 1 min )

In academia, for better or worse, we have what’s called a peer review system, where papers get accepted to journals, conferences, or other venues on the basis of reviews from other researchers, who ideally are subject area experts and thus are qualified to evaluate the paper. The reviewers also cannot have a conflict of interest with the authors, and should not be overwhelmed with too many papers to review. This is the ideal world, and is not always what happens in practice. From my experience in the robotics academic community (and this may apply to other disciplines), it generally seems like there is no standard definition of an “appropriate” or “maximum” reviewing load for a reviewer. This is difficult to define as different papers mandate different reviewing efforts; a massive journal …  ( 4 min )

A recent paper (Neural Networks, {\bf 132} (2020), 253-268) introduces a straightforward and simple kernel based approximation for manifold learning that does not require the knowledge of anything about the manifold, except for its dimension. In this paper, we examine how the pointwise error in approximation using least squares optimization based on similarly localized kernels depends upon the data characteristics and deteriorates as one goes away from the training data. The theory is presented with an abstract localized kernel, which can utilize any prior knowledge about the data being located on an unknown sub-manifold of a known manifold. We demonstrate the performance of our approach using a publicly available micro-Doppler data set, and investigate the use of different preprocessing measures, kernels, and manifold dimensions. Specifically, it is shown that the localized kernel introduced in the above mentioned paper when used with PCA components leads to a near-competitive performance to deep neural networks, and offers significant improvements in training speed and memory requirements. To demonstrate the fact that our methods are agnostic to the domain knowledge, we examine the classification problem in a simple video data set.

In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i.e. Schr\"odinger bridges). We advocate stochastic control as a finite time and low variance alternative to popular steady-state methods such as stochastic gradient Langevin dynamics (SGLD). Furthermore, we discuss and adapt the existing theoretical guarantees of this framework and establish connections to already existing VI routines in SDE-based models.

Sepsis is a life-threatening condition with organ dysfunction and is a leading cause of death and critical illness worldwide. Accurate detection of sepsis during emergency department triage would allow early initiation of lab analysis, antibiotic administration, and other sepsis treatment protocols. The purpose of this study was to determine whether EHR data can be extracted and synthesized with the latest machine learning algorithms (KATE Sepsis) and clinical natural language processing to produce accurate sepsis models, and compare KATE Sepsis performance with existing sepsis screening protocols, such as SIRS and qSOFA. A machine learning model (KATE Sepsis) was developed using patient encounters with triage data from 16 participating hospitals. KATE Sepsis, SIRS, standard screening (SIRS with source of infection) and qSOFA were tested in three settings. Cohort-A was a retrospective analysis on medical records from a single Site 1. Cohort-B was a prospective analysis of Site 1. Cohort-C was a retrospective analysis on Site 1 with 15 additional sites. Across all cohorts, KATE Sepsis demonstrates an AUC of 0.94-0.963 with 73-74.87% TPR and 3.76-7.17% FPR. Standard screening demonstrates an AUC of 0.682-0.726 with 39.39-51.19% TPR and 2.9-6.02% FPR. The qSOFA protocol demonstrates an AUC of 0.544-0.56, with 10.52-13.18% TPR and 1.22-1.68% FPR. For severe sepsis, across all cohorts, KATE Sepsis demonstrates an AUC of 0.935-0.972 with 70-82.26% TPR and 4.64-8.62% FPR. For septic shock, across all cohorts, KATE Sepsis demonstrates an AUC of 0.96-0.981 with 85.71-89.66% TPR and 4.85-8.8% FPR. SIRS, standard screening, and qSOFA demonstrate low AUC and TPR for severe sepsis and septic shock detection. KATE Sepsis provided substantially better sepsis detection performance in triage than commonly used screening protocols.

Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data. Deep learning has employed attention to boost performance for many applications. Interestingly, the same attention design can suit processing different data modalities and can easily be incorporated into large networks. Furthermore, multiple complementary attention mechanisms can be incorporated in one network. Hence, attention techniques have become extremely attractive. However, the literature lacks a comprehensive survey specific to attention techniques to guide researchers in employing attention in their deep models. Note that, besides being demanding in terms of training data and computational resources, transformers only cover a single category in self-attention out of the many categories available. We fill this gap and provide an in-depth survey of 50 attention techniques categorizing them by their most prominent features. We initiate our discussion by introducing the fundamental concepts behind the success of attention mechanism. Next, we furnish some essentials such as the strengths and limitations of each attention category, describe their fundamental building blocks, basic formulations with primary usage, and applications specifically for computer vision. We also discuss the challenges and open questions related to attention mechanism in general. Finally, we recommend possible future research directions for deep attention.

Deep learning has recently achieved initial success in program analysis tasks such as bug detection. Lacking real bugs, most existing works construct training and test data by injecting synthetic bugs into correct programs. Despite achieving high test accuracy (e.g. >90%), the resulting bug detectors are found to be surprisingly unusable in practice, i.e., <10% precision when used to scan real software repositories. In this work, we argue that this massive performance difference is caused by distribution shift, i.e., a fundamental mismatch between the real bug distribution and the synthetic bug distribution used to train and evaluate the detectors. To address this key challenge, we propose to train a bug detector in two phases, first on a synthetic bug distribution to adapt the model to the bug detection domain, and then on a real bug distribution to drive the model towards the real distribution. During these two phases, we leverage a multi-task hierarchy, focal loss, and contrastive learning to further boost performance. We evaluate our approach extensively on three widely studied bug types, for which we construct new datasets carefully designed to capture the real bug distribution. The results demonstrate that our approach is practically effective and successfully mitigates the distribution shift: our learned detectors are highly performant on both our constructed test set and the latest version of open source repositories.

Persuading people to change their opinions is a common practice in online discussion forums on topics ranging from political campaigns to relationship consultation. Enhancing people's ability to write persuasive arguments could not only practice their critical thinking and reasoning but also contribute to the effectiveness and civility in online communication. It is, however, not an easy task in online discussion settings where written words are the primary communication channel. In this paper, we derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions through a survey with 123 online forum users and interviews with five debating experts. To satisfy these design goals, we analyzed and built a labeled dataset of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and evidence) in 164 arguments with high ratings on persuasiveness from ChangeMyView, a popular online discussion forum. We then designed an interactive visual system, Persua, which provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments. In particular, the system constructs portfolios of arguments based on different persuasive strategies applied to a given discussion topic. It then presents concrete examples based on the difference between the portfolios of user input and high-quality arguments in the dataset. A between-subjects study shows suggestive evidence that Persua encourages users to submit more times for feedback and helps users improve more on the persuasiveness of their arguments than a baseline system. Finally, a set of design considerations was summarized to guide future intelligent systems that improve the persuasiveness in text.

Learning to hash pictures a list-wise sorting problem. Its testing metrics, e.g., mean-average precision, count on a sorted candidate list ordered by pair-wise code similarity. However, scarcely does one train a deep hashing model with the sorted results end-to-end because of the non-differentiable nature of the sorting operation. This inconsistency in the objectives of training and test may lead to sub-optimal performance since the training loss often fails to reflect the actual retrieval metric. In this paper, we tackle this problem by introducing Naturally-Sorted Hashing (NSH). We sort the Hamming distances of samples' hash codes and accordingly gather their latent representations for self-supervised training. Thanks to the recent advances in differentiable sorting approximations, the hash head receives gradients from the sorter so that the hash encoder can be optimized along with the training procedure. Additionally, we describe a novel Sorted Noise-Contrastive Estimation (SortedNCE) loss that selectively picks positive and negative samples for contrastive learning, which allows NSH to mine data semantic relations during training in an unsupervised manner. Our extensive experiments show the proposed NSH model significantly outperforms the existing unsupervised hashing methods on three benchmarked datasets.

Shapelet-based algorithms are widely used for time series classification because of their ease of interpretation, but they are currently outperformed by recent state-of-the-art approaches. We present a new formulation of time series shapelets including the notion of dilation, and we introduce a new shapelet feature to enhance their discriminative power for classification. Experiments performed on 112 datasets show that our method improves on the state-of-the-art shapelet algorithm, and achieves comparable accuracy to recent state-of-the-art approaches, without sacrificing neither scalability, nor interpretability.

Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency. This includes reward shaping, behavioral cloning, and reverse curriculum generation.

The reconstruction of electrons and photons in CMS depends on topological clustering of the energy deposited by an incident particle in different crystals of the electromagnetic calorimeter (ECAL). These clusters are formed by aggregating neighbouring crystals according to the expected topology of an electromagnetic shower in the ECAL. The presence of upstream material (beampipe, tracker and support structures) causes electrons and photons to start showering before reaching the calorimeter. This effect, combined with the 3.8T CMS magnetic field, leads to energy being spread in several clusters around the primary one. It is essential to recover the energy contained in these satellite clusters in order to achieve the best possible energy resolution for physics analyses. Historically satellite clusters have been associated to the primary cluster using a purely topological algorithm which does not attempt to remove spurious energy deposits from additional pileup interactions (PU). The performance of this algorithm is expected to degrade during LHC Run 3 (2022+) because of the larger average PU levels and the increasing levels of noise due to the ageing of the ECAL detector. New methods are being investigated that exploit state-of-the-art deep learning architectures like Graph Neural Networks (GNN) and self-attention algorithms. These more sophisticated models improve the energy collection and are more resilient to PU and noise, helping to preserve the electron and photon energy resolution achieved during LHC Runs 1 and 2. This work will cover the challenges of training the models as well the opportunity that this new approach offers to unify the ECAL energy measurement with the particle identification steps used in the global CMS photon and electron reconstruction.

Dissolved Gas-in-oil analysis (DGA) is used to monitor the condition of bushings on large power transformers. There are different techniques used in determining the conditions from the data collected, but in this work the Artificial Intelligence techniques are investigated. This work investigates which gases in DGA are related to each other and which ones are important for making decisions. When the related and crucial gases are determined, the other gases are discarded thereby reducing the number of attributes in DGA. Hence a further investigation is done to see how these new datasets influence the performance of the classifiers used to classify the DGA of full attributes. The classifiers used in these experiments were Backpropagation Neural Networks (BPNN) and Support Vector Machines (SVM) whereas the Principal Component Analysis (PCA), Rough Set (RS), Incremental Granular Ranking (GR++) and Decision Trees (DT) were used to reduce the attributes of the dataset. The parameters used when training the BPNN and SVM classifiers are kept fixed to create a controlled test environment when investigating the effects of reducing the number of gases. This work further introduced a new classifier that can handle high dimension dataset and noisy dataset, Rough Neural Network (RNN).

The syntactic categories of categorial grammar formalisms are structured units made of smaller, indivisible primitives, bound together by the underlying grammar's category formation rules. In the trending approach of constructive supertagging, neural models are increasingly made aware of the internal category structure, which in turn enables them to more reliably predict rare and out-of-vocabulary categories, with significant implications for grammars previously deemed too complex to find practical use. In this work, we revisit constructive supertagging from a graph-theoretic perspective, and propose a framework based on heterogeneous dynamic graph convolutions aimed at exploiting the distinctive structure of a supertagger's output space. We test our approach on a number of categorial grammar datasets spanning different languages and grammar formalisms, achieving substantial improvements over previous state of the art scores. Code will be made available at https://github.com/konstantinosKokos/dynamic-graph-supertagging

Industrial control systems (ICSs) are facing increasing cyber-physical attacks that can cause catastrophes in the physical system. Efficient anomaly detection models in the industrial sensor networks are essential for enhancing ICS reliability and security, due to the sensor data is related to the operational state of the ICS. Considering the limited availability of computing resources, this paper proposes a hybrid anomaly detection approach in cloud-edge collaboration industrial sensor networks. The hybrid approach consists of sensor data detection models deployed at the edges and a sensor data analysis model deployed in the cloud. The sensor data detection model based on Gaussian and Bayesian algorithms can detect the anomalous sensor data in real-time and upload them to the cloud for further analysis, filtering the normal sensor data and reducing traffic load. The sensor data analysis model based on Graph convolutional network, Residual algorithm and Long short-term memory network (GCRL) can effectively extract the spatial and temporal features and then identify the attack precisely. The proposed hybrid anomaly detection approach is evaluated using a benchmark dataset and baseline anomaly detection models. The experimental results show that the proposed approach can achieve an overall 11.19% increase in Recall and an impressive 14.29% improvement in F1-score, compared with the existing models.

Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. However, MTL must deal with challenges such as: overfitting to low resource tasks, catastrophic forgetting, and negative task transfer, or learning interference. Often, in Natural Language Processing (NLP), a separate model per task is needed to obtain the best performance. However, many fine-tuning approaches are both parameter inefficient, i.e., potentially involving one new model per task, and highly susceptible to losing knowledge acquired during pretraining. We propose a novel Transformer architecture consisting of a new conditional attention mechanism as well as a set of task-conditioned modules that facilitate weight sharing. Through this construction (a hypernetwork adapter), we achieve more efficient parameter sharing and mitigate forgetting by keeping half of the weights of a pretrained model fixed. We also use a new multi-task data sampling strategy to mitigate the negative effects of data imbalance across tasks. Using this approach, we are able to surpass single task fine-tuning methods while being parameter and data efficient (using around 66% of the data for weight updates). Compared to other BERT Large methods on GLUE, our 8-task model surpasses other Adapter methods by 2.8% and our 24-task model outperforms by 0.7-1.0% models that use MTL and single task fine-tuning. We show that a larger variant of our single multi-task model approach performs competitively across 26 NLP tasks and yields state-of-the-art results on a number of test and development sets. Our code is publicly available at https://github.com/CAMTL/CA-MTL.

The COVID-19 pandemic has dramatically changed how healthcare is delivered to patients, how patients interact with healthcare providers, and how healthcare information is disseminated to both healthcare providers and patients. Analytical models that were trained and tested pre-pandemic may no longer be performing up to expectations, providing unreliable and irrelevant learning (ML) models given that ML depends on the basic principle that what happened in the past are likely to repeat in the future. ML faced to two important degradation principles, concept drift, when the underlying properties and characteristics of the variables change and data drift, when the data distributions, probabilities, co-variates, and other variable relationships change, both of which are prime culprits of model failure. Therefore, detecting and diagnosing drift in existing models is something that has become an imperative. And perhaps even more important is a shift in our mindset towards a conscious recognition that drift is inevitable, and model building must incorporate intentional resilience, the ability to offset and recover quickly from failure, and proactive robustness, avoiding failure by developing models that are less vulnerable to drift and disruption.

Anomaly detection has many applications ranging from bank-fraud detection and cyber-threat detection to equipment maintenance and health monitoring. However, choosing a suitable algorithm for a given application remains a challenging design decision, often informed by the literature on anomaly detection algorithms. We extensively reviewed twelve of the most popular unsupervised anomaly detection methods. We observed that, so far, they have been compared using inconsistent protocols - the choice of the class of interest or the positive class, the split of training and test data, and the choice of hyperparameters - leading to ambiguous evaluations. This observation led us to define a coherent evaluation protocol which we then used to produce an updated and more precise picture of the relative performance of the twelve methods on five widely used tabular datasets. While our evaluation cannot pinpoint a method that outperforms all the others on all datasets, it identifies those that stand out and revise misconceived knowledge about their relative performances.

The COVID-19 pandemic has been severely impacting global society since December 2019. Massive research has been undertaken to understand the characteristics of the virus and design vaccines and drugs. The related findings have been reported in biomedical literature at a rate of about 10,000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200,000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g., Diagnosis and Treatment) to the articles in LitCovid. Despite the continuing advances in biomedical text mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset, consisting of over 30,000 articles with manually reviewed topics, was created for training and testing. It is one of the largest multilabel classification datasets in biomedical scientific literature. 19 teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181, and 0.9394 for macro F1-score, micro F1-score, and instance-based F1-score, respectively. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development.

We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an entirely automatic manner and simplifies an earlier supervised clustering approach [J. Chem. Theory Comput., 15, 6668 (2019)] by eliminating both the necessity for user-specified parameters and the training of an additional classifier. Unsupervised clustering results from GMM have the advantage of accurately reproducing chemically intuitive groupings of frontier molecular orbitals and having improved performance with an increasing number of training examples. The resulting clusters from supervised or unsupervised clustering is further combined with scalable Gaussian process regression (GPR) or linear regression (LR) to learn molecular energies accurately by generating a local regression model in each cluster. Among all four combinations of regressors and clustering methods, GMM combined with scalable exact Gaussian process regression (GMM/GPR) is the most efficient training protocol for MOB-ML. The numerical tests of molecular energy learning on thermalized datasets of drug-like molecules demonstrate the improved accuracy, transferability, and learning efficiency of GMM/GPR over not only other training protocols for MOB-ML, i.e., supervised regression-clustering combined with GPR(RC/GPR) and GPR without clustering. GMM/GPR also provide the best molecular energy predictions compared with the ones from literature on the same benchmark datasets. With a lower scaling, GMM/GPR has a 10.4-fold speedup in wall-clock training time compared with scalable exact GPR with a training size of 6500 QM7b-T molecules.

Online learning with expert advice is a fundamental problem of sequential prediction. In this problem, the algorithm has access to a set of $n$ "experts" who make predictions on each day. The goal on each day is to process these predictions, and make a prediction with the minimum cost. After making a prediction, the algorithm sees the actual outcome on that day, updates its state, and then moves on to the next day. An algorithm is judged by how well it does compared to the best expert in the set. The classical algorithm for this problem is the multiplicative weights algorithm. However, every application, to our knowledge, relies on storing weights for every expert, and uses $\Omega(n)$ memory. There is little work on understanding the memory required to solve the online learning with expert advice problem, or run standard sequential prediction algorithms, in natural streaming models, which is especially important when the number of experts, as well as the number of days on which the experts make predictions, is large. We initiate the study of the learning with expert advice problem in the streaming setting, and show lower and upper bounds. Our lower bound for i.i.d., random order, and adversarial order streams uses a reduction to a custom-built problem using a novel masking technique, to show a smooth trade-off for regret versus memory. Our upper bounds show novel ways to run standard sequential prediction algorithms in rounds on small "pools" of experts, thus reducing the necessary memory. For random-order streams, we show that our upper bound is tight up to low order terms. We hope that these results and techniques will have broad applications in online learning, and can inspire algorithms based on standard sequential prediction techniques, like multiplicative weights, for a wide range of other problems in the memory-constrained setting.

Denoising diffusion probabilistic models (DDPMs) have recently achieved leading performances in many generative tasks. However, the inherited iterative sampling process costs hindered their applications to speech synthesis. This paper proposes FastDiff, a fast conditional diffusion model for high-quality speech synthesis. FastDiff employs a stack of time-aware location-variable convolutions of diverse receptive field patterns to efficiently model long-term time dependencies with adaptive conditions. A noise schedule predictor is also adopted to reduce the sampling steps without sacrificing the generation quality. Based on FastDiff, we design an end-to-end text-to-speech synthesizer, FastDiff-TTS, which generates high-fidelity speech waveforms without any intermediate feature (e.g., Mel-spectrogram). Our evaluation of FastDiff demonstrates the state-of-the-art results with higher-quality (MOS 4.28) speech samples. Also, FastDiff enables a sampling speed of 58x faster than real-time on a V100 GPU, making diffusion models practically applicable to speech synthesis deployment for the first time. We further show that FastDiff generalized well to the mel-spectrogram inversion of unseen speakers, and FastDiff-TTS outperformed other competing methods in end-to-end text-to-speech synthesis. Audio samples are available at \url{https://FastDiff.github.io/}.

Due to the prosperity of Artificial Intelligence (AI) techniques, more and more backdoors are designed by adversaries to attack Deep Neural Networks (DNNs).Although the state-of-the-art method Neural Attention Distillation (NAD) can effectively erase backdoor triggers from DNNs, it still suffers from non-negligible Attack Success Rate (ASR) together with lowered classification ACCuracy (ACC), since NAD focuses on backdoor defense using attention features (i.e., attention maps) of the same order. In this paper, we introduce a novel backdoor defense framework named Attention Relation Graph Distillation (ARGD), which fully explores the correlation among attention features with different orders using our proposed Attention Relation Graphs (ARGs). Based on the alignment of ARGs between both teacher and student models during knowledge distillation, ARGD can eradicate more backdoor triggers than NAD. Comprehensive experimental results show that, against six latest backdoor attacks, ARGD outperforms NAD by up to 94.85% reduction in ASR, while ACC can be improved by up to 3.23%.

In this paper, we explore the usability of different natural language processing models for the sentiment analysis of social media applied to financial market prediction, using the cryptocurrency domain as a reference. We study how the different sentiment metrics are correlated with the price movements of Bitcoin. For this purpose, we explore different methods to calculate the sentiment metrics from a text finding most of them not very accurate for this prediction task. We find that one of the models outperforms more than 20 other public ones and makes it possible to fine-tune it efficiently given its interpretable nature. Thus we confirm that interpretable artificial intelligence and natural language processing methods might be more valuable practically than non-explainable and non-interpretable ones. In the end, we analyse potential causal connections between the different sentiment metrics and the price movements.

Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus on centralized data, where abundant and high-quality negative samples are easy to obtain. However, centralized user data storage and exploitation may lead to privacy risks and concerns, while decentralized user data on a single client can be too sparse and biased for accurate contrastive learning. In this paper, we propose a federated contrastive learning method named FedCL for privacy-preserving recommendation, which can exploit high-quality negative samples for effective model training with privacy well protected. We first infer user embeddings from local user data through the local model on each client, and then perturb them with local differential privacy (LDP) before sending them to a central server for hard negative sampling. Since individual user embedding contains heavy noise due to LDP, we propose to cluster user embeddings on the server to mitigate the influence of noise, and the cluster centroids are used to retrieve hard negative samples from the item pool. These hard negative samples are delivered to user clients and mixed with the observed negative samples from local data as well as in-batch negatives constructed from positive samples for federated model training. Extensive experiments on four benchmark datasets show FedCL can empower various recommendation methods in a privacy-preserving way.

Contrastive learning has gained popularity as an effective self-supervised representation learning technique. Several research directions improve traditional contrastive approaches, e.g., prototypical contrastive methods better capture the semantic similarity among instances and reduce the computational burden by considering cluster prototypes or cluster assignments, while adversarial instance-wise contrastive methods improve robustness against a variety of attacks. To the best of our knowledge, no prior work jointly considers robustness, cluster-wise semantic similarity and computational efficiency. In this work, we propose SwARo, an adversarial contrastive framework that incorporates cluster assignment permutations to generate representative adversarial samples. We evaluate SwARo on multiple benchmark datasets and against various white-box and black-box attacks, obtaining consistent improvements over state-of-the-art baselines.

Differentiable architecture search has gradually become the mainstream research topic in the field of Neural Architecture Search (NAS) for its high efficiency compared with the early NAS (EA-based, RL-based) methods. Recent differentiable NAS also aims at further improving the search performance and reducing the GPU-memory consumption. However, these methods are no longer naturally capable of tackling the non-differentiable objectives, e.g., energy, resource-constrained efficiency, and other metrics, let alone the multi-objective search demands. Researches in the multi-objective NAS field target this but requires vast computational resources cause of the sole optimization of each candidate architecture. In light of this discrepancy, we propose the TND-NAS, which is with the merits of the high efficiency in differentiable NAS framework and the compatibility among non-differentiable metrics in Multi-objective NAS. Under the differentiable NAS framework, with the continuous relaxation of the search space, TND-NAS has the architecture parameters ($\alpha$) been optimized in discrete space, while resorting to the progressive search space shrinking by $\alpha$. Our representative experiment takes two objectives (Parameters, Accuracy) as an example, we achieve a series of high-performance compact architectures on CIFAR10 (1.09M/3.3\%, 2.4M/2.95\%, 9.57M/2.54\%) and CIFAR100 (2.46M/18.3\%, 5.46/16.73\%, 12.88/15.20\%) datasets. Favorably, compared with other multi-objective NAS methods, TND-NAS is less time-consuming (1.3 GPU-days on NVIDIA 1080Ti, 1/6 of that in NSGA-Net), and can be conveniently adapted to real-world NAS scenarios (resource-constrained, platform-specialized).

Pretrained language models have achieved state-of-the-art performance when adapted to a downstream NLP task. However, theoretical analysis of these models is scarce and challenging since the pretraining and downstream tasks can be very different. We propose an analysis framework that links the pretraining and downstream tasks with an underlying latent variable generative model of text -- the downstream classifier must recover a function of the posterior distribution over the latent variables. We analyze head tuning (learning a classifier on top of the frozen pretrained model) and prompt tuning in this setting. The generative model in our analysis is either a Hidden Markov Model (HMM) or an HMM augmented with a latent memory component, motivated by long-term dependencies in natural language. We show that 1) under certain non-degeneracy conditions on the HMM, simple classification heads can solve the downstream task, 2) prompt tuning obtains downstream guarantees with weaker non-degeneracy conditions, and 3) our recovery guarantees for the memory-augmented HMM are stronger than for the vanilla HMM because task-relevant information is easier to recover from the long-term memory. Experiments on synthetically generated data from HMMs back our theoretical findings.  ( 2 min )

Fast-developing fields such as Artificial Intelligence (AI) often outpace the efforts of encyclopedic sources such as Wikipedia, which either do not completely cover recently-introduced topics or lack such content entirely. As a result, methods for automatically producing content are valuable tools to address this information overload. We show that recent advances in pretrained language modeling can be combined for a two-stage extractive and abstractive approach for Wikipedia lead paragraph generation. We extend this approach to generate longer Wikipedia-style summaries with sections and examine how such methods struggle in this application through detailed studies with 100 reference human-collected surveys. This is the first study on utilizing web resources for long Wikipedia-style summaries to the best of our knowledge.  ( 2 min )

We present CrissCross, a self-supervised framework for learning audio-visual representations. A novel notion is introduced in our framework whereby in addition to learning the intra-modal and standard synchronous cross-modal relations, CrissCross also learns asynchronous cross-modal relationships. We show that by relaxing the temporal synchronicity between the audio and visual modalities, the network learns strong generalized representations. Our experiments show that strong augmentations for both audio and visual modalities with relaxation of cross-modal temporal synchronicity optimize performance. To pretrain our proposed framework, we use 3 different datasets with varying sizes, Kinetics-Sound, Kinetics400, and AudioSet. The learned representations are evaluated on a number of downstream tasks namely action recognition, sound classification, and retrieval. CrissCross shows state-of-the-art performances on action recognition (UCF101 and HMDB51) and sound classification (ESC50 and DCASE). The codes and pretrained models will be made publicly available.  ( 2 min )

Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts. Most current evaluation methods for domain generalization (DG) adopt the leave-one-out strategy as a compromise on the limited number of domains. We propose a large-scale benchmark with extensive labeled domains named NICO++ along with more rational evaluation methods for comprehensively evaluating DG algorithms. To evaluate DG datasets, we propose two metrics to quantify covariate shift and concept shift, respectively. Two novel generalization bounds from the perspective of data construction are proposed to prove that limited concept shift and significant covariate shift favor the evaluation capability for generalization. Through extensive experiments, NICO++ shows its superior evaluation capability compared with current DG datasets and its contribution in alleviating unfairness caused by the leak of oracle knowledge in model selection.

Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving downstream tasks. However, most of those algorithms rely on a behavioural descriptor to characterise the diversity that is hand-coded, hence requiring prior knowledge about the considered tasks. In this work, we introduce Relevance-guided Unsupervised Discovery of Abilities; a Quality-Diversity algorithm that autonomously finds a behavioural characterisation tailored to the task at hand. In particular, our method introduces a custom diversity metric that leads to higher densities of solutions near the areas of interest in the learnt behavioural descriptor space. We evaluate our approach on a simulated robotic environment, where the robot has to autonomously discover its abilities based on its full sensory data. We evaluated the algorithms on three tasks: navigation to random targets, moving forward with a high velocity, and performing half-rolls. The experimental results show that our method manages to discover collections of solutions that are not only diverse, but also well-adapted to the considered downstream task.

Applying deep learning (DL) techniques in the electronic design automation (EDA) field has become a trending topic. Most solutions apply well-developed DL models to solve specific EDA problems. While demonstrating promising results, they require careful model tuning for every problem. The fundamental question on "How to obtain a general and effective neural representation of circuits?" has not been answered yet. In this work, we take the first step towards solving this problem. We propose DeepGate, a novel representation learning solution that effectively embeds both logic function and structural information of a circuit as vectors on each gate. Specifically, we propose transforming circuits into unified and-inverter graph format for learning and using signal probabilities as the supervision task in DeepGate. We then introduce a novel graph neural network that uses strong inductive biases in practical circuits as learning priors for signal probability prediction. Our experimental results show the efficacy and generalization capability of DeepGate.

The task of 2D human pose estimation is challenging as the number of keypoints is typically large (~ 17) and this necessitates the use of robust neural network architectures and training pipelines that can capture the relevant features from the input image. These features are then aggregated to make accurate heatmap predictions from which the final keypoints of human body parts can be inferred. Many papers in literature use CNN-based architectures for the backbone, and/or combine it with a transformer, after which the features are aggregated to make the final keypoint predictions [1]. In this paper, we consider the recently proposed Bottleneck Transformers [2], which combine CNN and multi-head self attention (MHSA) layers effectively, and we integrate it with a Transformer encoder and apply it to the task of 2D human pose estimation. We consider different backbone architectures and pre-train them using the DINO self-supervised learning method [3], this pre-training is found to improve the overall prediction accuracy. We call our model BTranspose, and experiments show that on the COCO validation set, our model achieves an AP of 76.4, which is competitive with other methods such as [1] and has fewer network parameters. Furthermore, we also present the dependencies of the final predicted keypoints on both the MHSA block and the Transformer encoder layers, providing clues on the image sub-regions the network attends to at the mid and high levels.

In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world. However, the open-world is vast and continuously changing, so it is not technically feasible to collect and annotate training datasets which accurately represent this domain. Therefore, there are always domain gaps between training datasets and the open-world which must be understood. In this work, we investigate the domain gaps between high-resolution and low-resolution LiDAR sensors in object detection networks. Using a unique dataset, which enables us to study sensor resolution domain gaps independent of other effects, we show two distinct domain gaps - an inference domain gap and a training domain gap. The inference domain gap is characterised by a strong dependence on the number of LiDAR points per object, while the training gap shows no such dependence. These fndings show that different approaches are required to close these inference and training domain gaps.

The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.

Invariance principle-based methods, for example, Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG). Despite the promising theory, invariance principle-based approaches fail in common classification tasks due to the mixture of the true invariant features and the spurious invariant features. In this paper, we propose a framework based on the conditional entropy minimization principle to filter out the spurious invariant features leading to a new algorithm with a better generalization capability. We theoretically prove that under some particular assumptions, the representation function can precisely recover the true invariant features. In addition, we also show that the proposed approach is closely related to the well-known Information Bottleneck (IB) framework. Both the theoretical and numerical results are provided to justify our approach.

Estimating the effects of continuous-valued interventions from observational data is critically important in fields such as climate science, healthcare, and economics. Recent work focuses on designing neural-network architectures and regularization functions to allow for scalable estimation of average and individual-level dose response curves from high-dimensional, large-sample data. Such methodologies assume ignorability (all confounding variables are observed) and positivity (all levels of treatment can be observed for every unit described by a given covariate value), which are especially challenged in the continuous treatment regime. Developing scalable sensitivity and uncertainty analyses that allow us to understand the ignorance induced in our estimates when these assumptions are relaxed receives less attention. Here, we develop a continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds that agree with both the observed data and a researcher-defined level of hidden confounding. We introduce a scalable algorithm to derive the bounds and uncertainty-aware deep models to efficiently estimate these bounds for high-dimensional, large-sample observational data. We validate our methods using both synthetic and real-world experiments. For the latter, we work in concert with climate scientists interested in evaluating the climatological impacts of human emissions on cloud properties using satellite observations from the past 15 years: a finite-data problem known to be complicated by the presence of a multitude of unobserved confounders.

NOTE: This preprint has a flawed theoretical formulation. Please avoid it and refer to the ICLR22 publication https://openreview.net/forum?id=q7n2RngwOM. Also, arXiv:2109.15062 contains some new ideas on unobserved Confounding. As an important problem of causal inference, we discuss the identification and estimation of treatment effects under unobserved confounding. Representing the confounder as a latent variable, we propose Intact-VAE, a new variant of variational autoencoder (VAE), motivated by the prognostic score that is sufficient for identifying treatment effects. We theoretically show that, under certain settings, treatment effects are identified by our model, and further, based on the identifiability of our model (i.e., determinacy of representation), our VAE is a consistent estimator with representation balanced for treatment groups. Experiments on (semi-)synthetic datasets show state-of-the-art performance under diverse settings.

For autonomous quadruped robot navigation in various complex environments, a typical SOTA system is composed of four main modules -- mapper, global planner, local planner, and command-tracking controller -- in a hierarchical manner. In this paper, we build a robust and safe local planner which is designed to generate a velocity plan to track a coarsely planned path from the global planner. Previous works used waypoint-based methods (e.g. Proportional-Differential control and pure pursuit) which simplify the path tracking problem to local point-goal navigation. However, they suffer from frequent collisions in geometrically complex and narrow environments because of two reasons; the global planner uses a coarse and inaccurate model and the local planner is unable to track the global plan sufficiently well. Currently, deep learning methods are an appealing alternative because they can learn safety and path feasibility from experience more accurately. However, existing deep learning methods are not capable of planning for a long horizon. In this work, we propose a learning-based fully autonomous navigation framework composed of three innovative elements: a learned forward dynamics model (FDM), an online sampling-based model-predictive controller, and an informed trajectory sampler (ITS). Using our framework, a quadruped robot can autonomously navigate in various complex environments without a collision and generate a smoother command plan compared to the baseline method. Furthermore, our method can reactively handle unexpected obstacles on the planned path and avoid them. Project page https://awesomericky.github.io/projects/FDM_ITS_navigation/.

Remote-sensing (RS) Change Detection (CD) aims to detect "changes of interest" from co-registered bi-temporal images. The performance of existing deep supervised CD methods is attributed to the large amounts of annotated data used to train the networks. However, annotating large amounts of remote sensing images is labor-intensive and expensive, particularly with bi-temporal images, as it requires pixel-wise comparisons by a human expert. On the other hand, we often have access to unlimited unlabeled multi-temporal RS imagery thanks to ever-increasing earth observation programs. In this paper, we propose a simple yet effective way to leverage the information from unlabeled bi-temporal images to improve the performance of CD approaches. More specifically, we propose a semi-supervised CD model in which we formulate an unsupervised CD loss in addition to the supervised Cross-Entropy (CE) loss by constraining the output change probability map of a given unlabeled bi-temporal image pair to be consistent under the small random perturbations applied on the deep feature difference map that is obtained by subtracting their latent feature representations. Experiments conducted on two publicly available CD datasets show that the proposed semi-supervised CD method can reach closer to the performance of supervised CD even with access to as little as 10% of the annotated training data. Code available at https://github.com/wgcban/SemiCD

We provide a general framework for constructing probability distributions on Riemannian manifolds, taking advantage of area-preserving maps and isometries. Control over distributions' properties, such as parameters, symmetry and modality yield a family of flexible distributions that are straightforward to sample from, suitable for use within Monte Carlo algorithms and latent variable models, such as autoencoders. As an illustration, we empirically validate our approach by utilizing our proposed distributions within a variational autoencoder and a latent space network model. Finally, we take advantage of the generalized description of this framework to posit questions for future work.

Pseudo-labels are confident predictions made on unlabeled target data by a classifier trained on labeled source data. They are widely used for adapting a model to unlabeled data, e.g., in a semi-supervised learning setting. Our key insight is that pseudo-labels are naturally imbalanced due to intrinsic data similarity, even when a model is trained on balanced source data and evaluated on balanced target data. If we address this previously unknown imbalanced classification problem arising from pseudo-labels instead of ground-truth training labels, we could remove model biases towards false majorities created by pseudo-labels. We propose a novel and effective debiased learning method with pseudo-labels, based on counterfactual reasoning and adaptive margins: The former removes the classifier response bias, whereas the latter adjusts the margin of each class according to the imbalance of pseudo-labels. Validated by extensive experimentation, our simple debiased learning delivers significant accuracy gains over the state-of-the-art on ImageNet-1K: 26% for semi-supervised learning with 0.2% annotations and 9% for zero-shot learning. Our code is available at: https://github.com/frank-xwang/debiased-pseudo-labeling.

Data augmentation via voice conversion (VC) has been successfully applied to low-resource expressive text-to-speech (TTS) when only neutral data for the target speaker are available. Although the quality of VC is crucial for this approach, it is challenging to learn a stable VC model because the amount of data is limited in low-resource scenarios, and highly expressive speech has large acoustic variety. To address this issue, we propose a novel data augmentation method that combines pitch-shifting and VC techniques. Because pitch-shift data augmentation enables the coverage of a variety of pitch dynamics, it greatly stabilizes training for both VC and TTS models, even when only 1,000 utterances of the target speaker's neutral data are available. Subjective test results showed that a FastSpeech 2-based emotional TTS system with the proposed method improved naturalness and emotional similarity compared with conventional methods.

Recent studies have demonstrated the effectiveness of the combination of machine learning and logical reasoning in inventing advanced artificial intelligence technologies. One-step abductive multi-target learning (OSAMTL), an approach that only combines machine learning and logical reasoning in a one-step balanced way, has as well shown its effectiveness in handling complex noisy labels of a single noisy sample in medical histopathology whole slide image analysis (MHWSIA). However, OSAMTL is not suitable for the situation where diverse noisy samples (DiNS) are provided for a learning task. In this paper, giving definition of DiNS, we propose one-step abductive multi-target learning with DiNS (OSAMTL-DiNS) to expand the original OSAMTL to handle complex noisy labels of DiNS. Applying OSAMTL-DiNS to tumour segmentation for breast cancer in MHWSIA, we show that OSAMTL-DiNS is able to enable various state-of-the-art approaches for learning from noisy labels to achieve more rational predictions.  ( 2 min )

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we develop a framework for using QML methods to simulate quantum dynamics on near-term quantum hardware. We use generalization bounds, which bound the error a machine learning model makes on unseen data, to rigorously analyze the training data requirements of an algorithm within this framework. This provides a guarantee that our algorithm is resource-efficient, both in terms of qubit and data requirements. Our numerics exhibit efficient scaling with problem size, and we simulate 20 times longer than Trotterization on IBMQ-Bogota.  ( 2 min )

Support vector machines (SVMs) are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, real-world problems may have an uneven class distribution. This article introduces EBCS-SVM: evolutionary bilevel cost-sensitive SVMs. EBCS-SVM handles imbalanced classification problems by simultaneously learning the support vectors and optimizing the SVM hyperparameters, which comprise the kernel parameter and misclassification costs. The resulting optimization problem is a bilevel problem, where the lower level determines the support vectors and the upper level the hyperparameters. This optimization problem is solved using an evolutionary algorithm (EA) at the upper level and sequential minimal optimization (SMO) at the lower level. These two methods work in a nested fashion, that is, the optimal support vectors help guide the search of the hyperparameters, and the lower level is initialized based on previous successful solutions. The proposed method is assessed using 70 datasets of imbalanced classification and compared with several state-of-the-art methods. The experimental results, supported by a Bayesian test, provided evidence of the effectiveness of EBCS-SVM when working with highly imbalanced datasets.  ( 2 min )

In this paper we present a new approach for feature fusion between RGB and LWIR Thermal images for the task of semantic segmentation for driving perception. We propose DooDLeNet, a double DeepLab architecture with specialized encoder-decoders for thermal and color modalities and a shared decoder for final segmentation. We combine two strategies for feature fusion: confidence weighting and correlation weighting. We report state-of-the-art mean IoU results on the MF dataset.  ( 2 min )

Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales. The development of graph convolutional networks (GCNs) has created the opportunity to address this information complexity via graph-driven architectures, since GCNs can perform feature aggregation, interaction, and reasoning with remarkable flexibility and efficiency. These GCNs capabilities have spawned a new wave of research in medical imaging analysis with the overarching goal of improving quantitative disease understanding, monitoring, and diagnosis. Yet daunting challenges remain for designing the important image-to-graph transformation for multi-modality medical imaging and gaining insights into model interpretation and enhanced clinical decision support. In this review, we present recent GCNs developments in the context of medical image analysis including imaging data from radiology and histopathology. We discuss the fast-growing use of graph network architectures in medical image analysis to improve disease diagnosis and patient outcomes in clinical practice. To foster cross-disciplinary research, we present GCNs technical advancements, emerging medical applications, identify common challenges in the use of image-based GCNs and their extensions in model interpretation, large-scale benchmarks that promise to transform the scope of medical image studies and related graph-driven medical research.  ( 2 min )

Discrete-time modeling of acoustic, mechanical and electrical systems is a prominent topic in the musical signal processing literature. Such models are mostly derived by discretizing a mathematical model, given in terms of ordinary or partial differential equations, using established techniques. Recent work has applied the techniques of machine-learning to construct such models automatically from data for the case of systems which have lumped states described by scalar values, such as electrical circuits. In this work, we examine how similar techniques are able to construct models of systems which have spatially distributed rather than lumped states. We describe several novel recurrent neural network structures, and show how they can be thought of as an extension of modal techniques. As a proof of concept, we generate synthetic data for three physical systems and show that the proposed network structures can be trained with this data to reproduce the behavior of these systems.

Compared with other imaging modalities, intravascular optical coherence tomography (IVOCT) has significant advantages for guiding percutaneous coronary interventions. To aid IVOCT research studies, we developed the Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) analysis software. To automate image analysis results, the software includes several important algorithmic steps: pre-processing, deep learning plaque segmentation, machine learning identification of stent struts, and registration of pullbacks. Interactive visualization and manual editing of segmentations were included in the software. Quantifications include stent deployment characteristics (e.g., stent strut malapposition), strut level analysis, calcium angle, and calcium thickness measurements. Interactive visualizations include (x,y) anatomical, en face, and longitudinal views with optional overlays. Underlying plaque segmentation algorithm yielded excellent pixel-wise results (86.2% sensitivity and 0.781 F1 score). Using OCTOPUS on 34 new pullbacks, we determined that following automated segmentation, only 13% and 23% of frames needed any manual touch up for detailed lumen and calcification labeling, respectively. Only up to 3.8% of plaque pixels were modified, leading to an average editing time of only 7.5 seconds/frame, an approximately 80% reduction compared to manual analysis. Regarding stent analysis, sensitivity and precision were both greater than 90%, and each strut was successfully classified as either covered or uncovered with high sensitivity (94%) and specificity (90%). We introduced and evaluated the clinical application of a highly automated software package, OCTOPUS, for quantitative plaque and stent analysis in IVOCT images. The software is currently used as an offline tool for research purposes; however, the software's embedded algorithms may also be useful for real-time treatment planning.  ( 2 min )

The freeform architectural modeling process often involves two important stages: concept design and digital modeling. In the first stage, architects usually sketch the overall 3D shape and the panel layout on a physical or digital paper briefly. In the second stage, a digital 3D model is created using the sketch as a reference. The digital model needs to incorporate geometric requirements for its components, such as the planarity of panels due to consideration of construction costs, which can make the modeling process more challenging. In this work, we present a novel sketch-based system to bridge the concept design and digital modeling of freeform roof-like shapes represented as planar quadrilateral (PQ) meshes. Our system allows the user to sketch the surface boundary and contour lines under axonometric projection and supports the sketching of occluded regions. In addition, the user can sketch feature lines to provide directional guidance to the PQ mesh layout. Given the 2D sketch input, we propose a deep neural network to infer in real-time the underlying surface shape along with a dense conjugate direction field, both of which are used to extract the final PQ mesh. To train and validate our network, we generate a large synthetic dataset that mimics architect sketching of freeform quadrilateral patches. The effectiveness and usability of our system are demonstrated with quantitative and qualitative evaluation as well as user studies.  ( 2 min )

Given an increasingly volatile climate, the relationship between weather and transit ridership has drawn increasing interest. However, challenges stemming from spatio-temporal dependency and non-stationarity have not been fully addressed in modelling and predicting transit ridership under the influence of weather conditions especially with the traditional statistical approaches. Drawing on three-month smart card data in Brisbane, Australia, this research adopts and assesses a suite of machine-learning algorithms, i.e., random forest, eXtreme Gradient Boosting (XGBoost) and Tweedie XGBoost, to model and predict near real-time bus ridership in relation to sudden change of weather conditions. The study confirms that there indeed exists a significant level of spatio-temporal variability of weather-ridership relationship, which produces equally dynamic patterns of prediction errors. Further comparison of model performance suggests that Tweedie XGBoost outperforms the other two machine-learning algorithms in generating overall more accurate prediction outcomes in space and time. Future research may advance the current study by drawing on larger data sets and applying more advanced machine and deep-learning approaches to provide more enhanced evidence for real-time operation of transit systems.  ( 2 min )

The 2021 Speaker Recognition Evaluation (SRE21) was the latest cycle of the ongoing evaluation series conducted by the U.S. National Institute of Standards and Technology (NIST) since 1996. It was the second large-scale multimodal speaker/person recognition evaluation organized by NIST (the first one being SRE19). Similar to SRE19, it featured two core evaluation tracks, namely audio and audio-visual, as well as an optional visual track. In addition to offering fixed and open training conditions, it also introduced new challenges for the community, thanks to a new multimodal (i.e., audio, video, and selfie images) and multilingual (i.e., with multilingual speakers) corpus, termed WeCanTalk, collected outside North America by the Linguistic Data Consortium (LDC). These challenges included: 1) trials (target and non-target) with enrollment and test segments originating from different domains (i.e., telephony versus video), and 2) trials (target and non-target) with enrollment and test segments spoken in different languages (i.e., cross-lingual trials). This paper presents an overview of SRE21 including the tasks, performance metric, data, evaluation protocol, results and system performance analyses. A total of 23 organizations (forming 15 teams) from academia and industry participated in SRE21 and submitted 158 valid system outputs. Evaluation results indicate: audio-visual fusion produce substantial gains in performance over audio-only or visual-only systems; top performing speaker and face recognition systems exhibited comparable performance under the matched domain conditions present in this evaluation; and, the use of complex neural network architectures (e.g., ResNet) along with angular losses with margin, data augmentation, as well as long duration fine-tuning contributed to notable performance improvements for the audio-only speaker recognition task.  ( 2 min )

In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of radio galaxies. We test if SSL with fewer labels can achieve test accuracies comparable to the supervised state-of-the-art and whether this holds when incorporating previously unseen data. We find that for the radio galaxy classification problem considered, SSL provides additional regularisation and outperforms the baseline test accuracy. However, in contrast to model performance metrics reported on computer science benchmarking data-sets, we find that improvement is limited to a narrow range of label volumes, with performance falling off rapidly at low label volumes. Additionally, we show that SSL does not improve model calibration, regardless of whether classification is improved. Moreover, we find that when different underlying catalogues drawn from the same radio survey are used to provide the labelled and unlabelled data-sets required for SSL, a significant drop in classification performance is observered, highlighting the difficulty of applying SSL techniques under dataset shift. We show that a class-imbalanced unlabelled data pool negatively affects performance through prior probability shift, which we suggest may explain this performance drop, and that using the Frechet Distance between labelled and unlabelled data-sets as a measure of data-set shift can provide a prediction of model performance, but that for typical radio galaxy data-sets with labelled sample volumes of O(1000), the sample variance associated with this technique is high and the technique is in general not sufficiently robust to replace a train-test cycle.  ( 2 min )

Despite its popularity in the reinforcement learning community, a provably convergent policy gradient method for general continuous space-time stochastic control problems has been elusive. This paper closes the gap by proposing a proximal gradient algorithm for feedback controls of finite-time horizon stochastic control problems. The state dynamics are continuous time nonlinear diffusions with controlled drift and possibly degenerate noise, and the objectives are nonconvex in the state and nonsmooth in the control. We prove under suitable conditions that the algorithm converges linearly to a stationary point of the control problem, and is stable with respect to policy updates by approximate gradient steps. The convergence result justifies the recent reinforcement learning heuristics that adding entropy regularization or a fictitious discount factor to the optimization objective accelerates the convergence of policy gradient methods. The proof exploits careful regularity estimates of backward stochastic differential equations.  ( 2 min )

Machine learning training methods depend plentifully and intricately on hyperparameters, motivating automated strategies for their optimisation. Many existing algorithms restart training for each new hyperparameter choice, at considerable computational cost. Some hypergradient-based one-pass methods exist, but these either cannot be applied to arbitrary optimiser hyperparameters (such as learning rates and momenta) or take several times longer to train than their base models. We extend these existing methods to develop an approximate hypergradient-based hyperparameter optimiser which is applicable to any continuous hyperparameter appearing in a differentiable model weight update, yet requires only one training episode, with no restarts. We also provide a motivating argument for convergence to the true hypergradient, and perform tractable gradient-based optimisation of independent learning rates for each model parameter. Our method performs competitively from varied random hyperparameter initialisations on several UCI datasets and Fashion-MNIST (using a one-layer MLP), Penn Treebank (using an LSTM) and CIFAR-10 (using a ResNet-18), in time only 2-3x greater than vanilla training.

We describe how the Fink broker early supernova Ia classifier optimizes its ML classifications by employing an active learning (AL) strategy. We demonstrate the feasibility of implementation of such strategies in the current Zwicky Transient Facility (ZTF) public alert data stream. We compare the performance of two AL strategies: uncertainty sampling and random sampling. Our pipeline consists of 3 stages: feature extraction, classification and learning strategy. Starting from an initial sample of 10 alerts (5 SN Ia and 5 non-Ia), we let the algorithm identify which alert should be added to the training sample. The system is allowed to evolve through 300 iterations. Our data set consists of 23 840 alerts from the ZTF with confirmed classification via cross-match with SIMBAD database and the Transient name server (TNS), 1 600 of which were SNe Ia (1 021 unique objects). The data configuration, after the learning cycle was completed, consists of 310 alerts for training and 23 530 for testing. Averaging over 100 realizations, the classifier achieved 89% purity and 54% efficiency. From 01/November/2020 to 31/October/2021 Fink has applied its early supernova Ia module to the ZTF stream and communicated promising SN Ia candidates to the TNS. From the 535 spectroscopically classified Fink candidates, 459 (86%) were proven to be SNe Ia. Our results confirm the effectiveness of active learning strategies for guiding the construction of optimal training samples for astronomical classifiers. It demonstrates in real data that the performance of learning algorithms can be highly improved without the need of extra computational resources or overwhelmingly large training samples. This is, to our knowledge, the first application of AL to real alerts data.

Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system. While the recurrent nature of these networks allows them to model arbitrarily long memories in the time series used in training, it makes it harder to impose prior knowledge or intuition through generic constraints. In this work, we present a path sampling approach based on principle of Maximum Caliber that allows us to include generic thermodynamic or kinetic constraints into recurrent neural networks. We show the method here for a widely used type of recurrent neural network known as long short-term memory network in the context of supplementing time series collected from different application domains. These include classical Molecular Dynamics of a protein and Monte Carlo simulations of an open quantum system continuously losing photons to the environment and displaying Rabi oscillations. Our method can be easily generalized to other generative artificial intelligence models and to generic time series in different areas of physical and social sciences, where one wishes to supplement limited data with intuition or theory based corrections.

We study the theory of neural network (NN) from the lens of classical nonparametric regression problems with a focus on NN's ability to adaptively estimate functions with heterogeneous smoothness -- a property of functions in Besov or Bounded Variation (BV) classes. Existing work on this problem requires tuning the NN architecture based on the function spaces and sample sizes. We consider a "Parallel NN" variant of deep ReLU networks and show that the standard weight decay is equivalent to promoting the $\ell_p$-sparsity ($0<p<1$) of the coefficient vector of an end-to-end learned function bases, i.e., a dictionary. Using this equivalence, we further establish that by tuning only the weight decay, such Parallel NN achieves an estimation error arbitrarily close to the minimax rates for both the Besov and BV classes. Notably, it gets exponentially closer to minimax optimal as the NN gets deeper. Our research sheds new lights on why depth matters and how NNs are more powerful than kernel methods.

Feature-based transfer is one of the most effective methodologies for transfer learning. Existing studies usually assume that the learned new feature representation is \emph{domain-invariant}, and thus train a transfer model $\mathcal{M}$ on the source domain. In this paper, we consider a more realistic scenario where the new feature representation is suboptimal and small divergence still exists across domains. We propose a new transfer model called Randomized Transferable Machine (RTM) to handle such small divergence of domains. Specifically, we work on the new source and target data learned from existing feature-based transfer methods. The key idea is to enlarge source training data populations by randomly corrupting the new source data using some noises, and then train a transfer model $\widetilde{\mathcal{M}}$ that performs well on all the corrupted source data populations. In principle, the more corruptions are made, the higher the probability of the new target data can be covered by the constructed source data populations, and thus better transfer performance can be achieved by $\widetilde{\mathcal{M}}$. An ideal case is with infinite corruptions, which however is infeasible in reality. We develop a marginalized solution that enables to train an $\widetilde{\mathcal{M}}$ without conducting any corruption but equivalent to be trained using infinite source noisy data populations. We further propose two instantiations of $\widetilde{\mathcal{M}}$, which theoretically show the transfer superiority over the conventional transfer model $\mathcal{M}$. More importantly, both instantiations have closed-form solutions, leading to a fast and efficient training process. Experiments on various real-world transfer tasks show that RTM is a promising transfer model.

Graph convolutional networks (GCNs) and their variants have achieved great success in dealing with graph-structured data. Nevertheless, it is well known that deep GCNs suffer from the over-smoothing problem, where node representations tend to be indistinguishable as more layers are stacked up. The theoretical research to date on deep GCNs has focused primarily on expressive power rather than trainability, an optimization perspective. Compared to expressivity, trainability attempts to address a more fundamental question: Given a sufficiently expressive space of models, can we successfully find a good solution via gradient descent-based optimizers? This work fills this gap by exploiting the Graph Neural Tangent Kernel (GNTK), which governs the optimization trajectory under gradient descent for wide GCNs. We formulate the asymptotic behaviors of GNTK in the large depth, which enables us to reveal the dropping trainability of wide and deep GCNs at an exponential rate in the optimization process. Additionally, we extend our theoretical framework to analyze residual connection-based techniques, which are found to be merely able to mitigate the exponential decay of trainability mildly. Inspired by our theoretical insights on trainability, we propose Critical DropEdge, a connectivity-aware and graph-adaptive sampling method, to alleviate the exponential decay problem more fundamentally. Experimental evaluation consistently confirms using our proposed method can achieve better results compared to relevant counterparts with both infinite-width and finite-width.

Modeling complex phenomena typically involves the use of both discrete and continuous variables. Such a setting applies across a wide range of problems, from identifying trends in time-series data to performing effective compositional scene understanding in images. Here, we propose Hybrid Memoised Wake-Sleep (HMWS), an algorithm for effective inference in such hybrid discrete-continuous models. Prior approaches to learning suffer as they need to perform repeated expensive inner-loop discrete inference. We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization. We evaluate HMWS in the GP-kernel learning and 3D scene understanding domains, and show that it outperforms current state-of-the-art inference methods.

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are assumed to be drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a distribution different from the training distribution. In this work, we prove out-of-distribution generalization for the task of learning an unknown unitary using a QNN and for a broad class of training and testing distributions. In particular, we show that one can learn the action of a unitary on entangled states using only product state training data. We numerically illustrate this by showing that the evolution of a Heisenberg spin chain can be learned using only product training states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics using near term quantum computers and quantum experiments, and further opens up new methods for both the classical and quantum compilation of quantum circuits.  ( 2 min )

For multi-band wireless ad hoc networks of multiple users, an anti-jamming game between the users and a jammer is studied. In this game, the users (resp. jammer) want to maximize (resp. minimize) the expected rewards of the users taking into account various factors such as communication rate, hopping cost, and jamming loss. We analyze the arms race of the game and derive an optimal frequency hopping policy at each stage of the arms race based on the Markov decision process (MDP). It is analytically shown that the arms race reaches an equilibrium after a few rounds, and a frequency hopping policy and a jamming strategy at the equilibrium are characterized. We propose two kinds of collision avoidance protocols to ensure that at most one user communicates in each frequency band, and provide various numerical results that show the effects of the reward parameters and collision avoidance protocols on the optimal frequency hopping policy and the expected rewards at the equilibrium. Moreover, we discuss about equilibria for the case where the jammer adopts some unpredictable jamming strategies.

Reliable wireless communication between the autonomously driving cars is one of the fundamental needs for guaranteeing passenger safety and comfort. However, when the number of communicating cars increases, the transmission quality may be significantly degraded due to too high occupancy radio of the used frequency band. In this paper, we concentrate on the autonomous vehicle-platooning use-case, where intra-platoon communication is done in the dynamically selected frequency band, other than nominally devoted for such purposes. The carrier selection is done in a flexible manner with the support of the context database located at the roadside unit (edge of wireless communication infrastructure). However, as the database delivers only context information to the platoons' leaders, the final decision is made separately by the individual platoons, following the suggestions made by the artificial intelligence algorithms. In this work, we concentrate on a lightweight Q-learning solution, that could be successfully implemented in each car for dynamic channel selection.

We propose a simple scheme for merging two neural networks trained with different starting initialization into a single one with the same size as the original ones. We do this by carefully selecting channels from each input network. Our procedure might be used as a finalization step after one tries multiple starting seeds to avoid an unlucky one. We also show that training two networks and merging them leads to better performance than training a single network for an extended period of time. Availability: https://github.com/fmfi-compbio/neural-network-merging

We propose OUR-GAN, the first one-shot ultra-high-resolution (UHR) image synthesis framework that generates non-repetitive images with 4K or higher resolution from a single training image. OUR-GAN generates a visually coherent image at low resolution and then gradually increases the resolution by super-resolution. Since OUR-GAN learns from a real UHR image, it can synthesize large-scale shapes with fine details while maintaining long-range coherence, which is difficult with conventional generative models that generate large images based on the patch distribution learned from relatively small images. OUR-GAN applies seamless subregion-wise super-resolution that synthesizes 4k or higher UHR images with limited memory, preventing discontinuity at the boundary. Additionally, OUR-GAN improves visual coherence maintaining diversity by adding vertical positional embeddings to the feature maps. In experiments on the ST4K and RAISE datasets, OUR-GAN exhibited improved fidelity, visual coherency, and diversity compared with existing methods. The synthesized images are presented at https://anonymous-62348.github.io.

We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a software debugger, a user can continue or or halt an episode at any time step and inspect parameters and probability distributions of interest. The system enables insight into the structure of a given policy and in the behavior of a policy in edge cases. We demonstrate the system with a network intrusion use case. We examine the evolution of an IT infrastructure's state and the actions prescribed by security policies while an attack occurs. The policies for the demonstration have been obtained through a reinforcement learning approach that includes a simulation system where policies are incrementally learned and an emulation system that produces statistics that drive the simulation runs.

WLANs, which have overtaken wired networks to become the primary means of connecting devices to the Internet, are prone to performance issues due to the scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and subsequent amendments aim at increasing the spatial reuse of a radio channel by allowing the dynamic update of two key parameters in wireless transmission: the transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this paper, we present INSPIRE, a distributed solution performing local Bayesian optimizations based on Gaussian processes to improve the spatial reuse in WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and favors altruistic behaviors of the access points, leading them to find adequate configurations of their TX_POWER and OBSS_PD parameters for the "greater good" of the WLANs. We demonstrate the superiority of INSPIRE over other state-of-the-art strategies using the ns-3 simulator and two examples inspired by real-life deployments of dense WLANs. Our results show that, in only a few seconds, INSPIRE is able to drastically increase the quality of service of operational WLANs by improving their fairness and throughput.

Cryptocurrencies are no longer just the preferred option for cybercriminal activities on darknets, due to the increasing adoption in mainstream applications. This is partly due to the transparency associated with the underpinning ledgers, where any individual can access the record of a transaction record on the public ledger. In this paper, we build a dataset comprising Bitcoin transactions between 12 July 2019 and 26 May 2021. This dataset (hereafter referred to as BABD-13) contains 13 types of Bitcoin addresses, 5 categories of indicators with 148 features, and 544,462 labeled data. We then use our proposed dataset on common machine learning models, namely: k-nearest neighbors algorithm, decision tree, random forest, multilayer perceptron, and XGBoost. The results show that the accuracy rates of these machine learning models on our proposed dataset are between 93.24% and 96.71%. We also analyze the proposed features and their relationships from the experiments, and propose a k-hop subgraph generation algorithm to extract a k-hop subgraph from the entire Bitcoin transaction graph constructed by the directed heterogeneous multigraph starting from a specific Bitcoin address node (e.g., a known transaction associated with a criminal investigation).

Transfer learning is a classic paradigm by which models pretrained on large "upstream" datasets are adapted to yield good results on "downstream" specialized datasets. Generally, more accurate models on the "upstream" dataset tend to provide better transfer accuracy "downstream". In this work, we perform an in-depth investigation of this phenomenon in the context of convolutional neural networks (CNNs) trained on the ImageNet dataset, which have been pruned - that is, compressed by sparsifying their connections. We consider transfer using unstructured pruned models obtained by applying several state-of-the-art pruning methods, including magnitude-based, second-order, re-growth, lottery-ticket, and regularization approaches, in the context of twelve standard transfer tasks. In a nutshell, our study shows that sparse models can match or even outperform the transfer performance of dense models, even at high sparsities, and, while doing so, can lead to significant inference and even training speedups. At the same time, we observe and analyze significant differences in the behaviour of different pruning methods.

We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IPS) estimation. However, the performance of existing propensity-based methods can suffer significantly from the propensity estimation bias. In fact, most of the previous IPS-based methods require some amount of missing-completely-at-random (MCAR) data to accurately estimate the propensity. This leads to a critical self-contradiction; IPS is ineffective without MCAR data, even though it originally aims to learn recommenders from only missing-not-at-random feedback. To resolve this propensity contradiction, we derive a propensity-independent generalization error bound and propose a novel algorithm to minimize the theoretical bound via adversarial learning. Our theory and algorithm do not require a propensity estimation procedure, thereby leading to a well-performing rating predictor without the true propensity information. Extensive experiments demonstrate that the proposed approach is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data.  ( 2 min )

Advances in deep vision techniques and ubiquity of smart cameras will drive the next generation of video analytics. However, video analytics applications consume vast amounts of energy as both deep learning techniques and cameras are power-hungry. In this paper, we focus on a parking video analytics platform and propose RL-CamSleep, a deep reinforcement learning-based technique, to actuate the cameras to reduce the energy footprint while retaining the system's utility. Our key insight is that many video-analytics applications do not always need to be operational, and we can design policies to activate video analytics only when necessary. Moreover, our work is complementary to existing work that focuses on improving hardware and software efficiency. We evaluate our approach on a city-scale parking dataset having 76 streets spread across the city. Our analysis demonstrates how streets have various parking patterns, highlighting the importance of an adaptive policy. Our approach can learn such an adaptive policy that can reduce the average energy consumption by 76.38% and achieve an average accuracy of more than 98% in performing video analytics.

Despite progress across a broad range of applications, Transformers have limited success in systematic generalization. The situation is especially frustrating in the case of algorithmic tasks, where they often fail to find intuitive solutions that route relevant information to the right node/operation at the right time in the grid represented by Transformer columns. To facilitate the learning of useful control flow, we propose two modifications to the Transformer architecture, copy gate and geometric attention. Our novel Neural Data Router (NDR) achieves 100% length generalization accuracy on the classic compositional table lookup task, as well as near-perfect accuracy on the simple arithmetic task and a new variant of ListOps testing for generalization across computational depths. NDR's attention and gating patterns tend to be interpretable as an intuitive form of neural routing. Our code is public.

Recent studies show that deep neural networks (DNN) are vulnerable to adversarial examples, which aim to mislead DNNs by adding perturbations with small magnitude. To defend against such attacks, both empirical and theoretical defense approaches have been extensively studied for a single ML model. In this work, we aim to analyze and provide the certified robustness for ensemble ML models, together with the sufficient and necessary conditions of robustness for different ensemble protocols. Although ensemble models are shown more robust than a single model empirically; surprisingly, we find that in terms of the certified robustness the standard ensemble models only achieve marginal improvement compared to a single model. Thus, to explore the conditions that guarantee to provide certifiably robust ensemble ML models, we first prove that diversified gradient and large confidence margin are sufficient and necessary conditions for certifiably robust ensemble models under the model-smoothness assumption. We then provide the bounded model-smoothness analysis based on the proposed Ensemble-before-Smoothing strategy. We also prove that an ensemble model can always achieve higher certified robustness than a single base model under mild conditions. Inspired by the theoretical findings, we propose the lightweight Diversity Regularized Training (DRT) to train certifiably robust ensemble ML models. Extensive experiments show that our DRT enhanced ensembles can consistently achieve higher certified robustness than existing single and ensemble ML models, demonstrating the state-of-the-art certified L2-robustness on MNIST, CIFAR-10, and ImageNet datasets.  ( 2 min )

Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly used in several fields to improve performance and the level of automation. In recent years, this use has exponentially increased due to the advancement of high-performance computing and the ever increasing size of data. One of such fields is that of hardware design; specifically the design of digital and analog integrated circuits~(ICs), where AI/ ML techniques have been extensively used to address ever-increasing design complexity, aggressive time-to-market, and the growing number of ubiquitous interconnected devices (IoT). However, the security concerns and issues related to IC design have been highly overlooked. In this paper, we summarize the state-of-the-art in AL/ML for circuit design/optimization, security and engineering challenges, research in security-aware CAD/EDA, and future research directions and needs for using AI/ML for security-aware circuit design.  ( 2 min )

Retrieving accurate semantic information in challenging high dynamic range (HDR) and high-speed conditions remains an open challenge for image-based algorithms due to severe image degradations. Event cameras promise to address these challenges since they feature a much higher dynamic range and are resilient to motion blur. Nonetheless, semantic segmentation with event cameras is still in its infancy which is chiefly due to the novelty of the sensor, and the lack of high-quality, labeled datasets. In this work, we introduce ESS, which tackles this problem by directly transferring the semantic segmentation task from existing labeled image datasets to unlabeled events via unsupervised domain adaptation (UDA). Compared to existing UDA methods, our approach aligns recurrent, motion-invariant event embeddings with image embeddings. For this reason, our method neither requires video data nor per-pixel alignment between images and events and, crucially, does not need to hallucinate motion from still images. Additionally, to spur further research in event-based semantic segmentation, we introduce DSEC-Semantic, the first large-scale event-based dataset with fine-grained labels. We show that using image labels alone, ESS outperforms existing UDA approaches, and when combined with event labels, it even outperforms state-of-the-art supervised approaches on both DDD17 and DSEC-Semantic. Finally, ESS is general-purpose, which unlocks the vast amount of existing labeled image datasets and paves the way for new and exciting research directions in new fields previously inaccessible for event cameras.  ( 2 min )

An ability to predict the popularity dynamics of individual items within a complex evolving system has important implications in a wide range of domains. Here we propose a deep learning attention mechanism to model the process through which individual items gain their popularity. We analyze the interpretability of the model with the four key phenomena confirmed independently in the previous studies of long-term popularity dynamics quantification, including the intrinsic quality, the aging effect, the recency effect and the Matthew effect. We analyze the effectiveness of introducing attention model in popularity dynamics prediction. Extensive experiments on a real-large citation data set demonstrate that the designed deep learning attention mechanism possesses remarkable power at predicting the long-term popularity dynamics. It consistently outperforms the existing methods, and achieves a significant performance improvement.  ( 2 min )

Active learning frameworks offer efficient data annotation without remarkable accuracy degradation. In other words, active learning starts training the model with a small size of labeled data while exploring the space of unlabeled data in order to select most informative samples to be labeled. Generally speaking, representing the uncertainty is crucial in any active learning framework, however, deep learning methods are not capable of either representing or manipulating model uncertainty. On the other hand, from the real world application perspective, uncertainty representation is getting more and more attention in the machine learning community. Deep Bayesian active learning frameworks and generally any Bayesian active learning settings, provide practical consideration in the model which allows training with small data while representing the model uncertainty for further efficient training. In this paper, we briefly survey recent advances in Bayesian active learning and in particular deep Bayesian active learning frameworks.

Self-adaptive systems frequently use tactics to perform adaptations. Tactic examples include the implementation of additional security measures when an intrusion is detected, or activating a cooling mechanism when temperature thresholds are surpassed. Tactic volatility occurs in real-world systems and is defined as variable behavior in the attributes of a tactic, such as its latency or cost. A system's inability to effectively account for tactic volatility adversely impacts its efficiency and resiliency against the dynamics of real-world environments. To enable systems' efficiency against tactic volatility, we propose a Tactic Volatility Aware (TVA-E) process utilizing evolved Recurrent Neural Networks (eRNN) to provide accurate tactic predictions. TVA-E is also the first known process to take advantage of uncertainty reduction tactics to provide additional information to the decision-making process and reduce uncertainty. TVA-E easily integrates into popular adaptation processes enabling it to immediately benefit a large number of existing self-adaptive systems. Simulations using 52,106 tactic records demonstrate that: I) eRNN is an effective prediction mechanism, II) TVA-E represents an improvement over existing state-of-the-art processes in accounting for tactic volatility, and III) Uncertainty reduction tactics are beneficial in accounting for tactic volatility. The developed dataset and tool can be found at https://tacticvolatility.github.io/

We propose a parameter sharing method for Transformers (Vaswani et al., 2017). The proposed approach relaxes a widely used technique, which shares parameters for one layer with all layers such as Universal Transformers (Dehghani et al., 2019), to increase the efficiency in the computational time. We propose three strategies: Sequence, Cycle, and Cycle (rev) to assign parameters to each layer. Experimental results show that the proposed strategies are efficient in the parameter size and computational time. Moreover, we indicate that the proposed strategies are also effective in the configuration where we use many training data such as the recent WMT competition.  ( 2 min )

Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also faces some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that these problems can be alleviated by random dropping methods, which integrate noises into models by randomly masking parts of the input. However, some open-ended problems of random dropping on GNNs remain to solve. First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models. Second, random noises introduced to GNNs cause the incomplete coverage of parameters and unstable training process. In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the message matrix and can be applied to any message-passing GNNs. Furthermore, we elaborate the superiority of DropMessage: it stabilizes the training process by reducing sample variance; it keeps information diversity from the perspective of information theory, which makes it a theoretical upper bound of other methods. Also, we unify existing random dropping methods into our framework and analyze their effects on GNNs. To evaluate our proposed method, we conduct experiments that aims for multiple tasks on five public datasets and two industrial datasets with various backbone models. The experimental results show that DropMessage has both advantages of effectiveness and generalization.  ( 2 min )

Graph-based spatio-temporal neural networks are effective to model the spatial dependency among discrete points sampled irregularly from unstructured grids, thanks to the great expressiveness of graph neural networks. However, these models are usually spatially-transductive -- only fitting the signals for discrete spatial nodes fed in models but unable to generalize to `unseen' spatial points with zero-shot. In comparison, for forecasting tasks on continuous space such as temperature prediction on the earth's surface, the \textit{spatially-inductive} property allows the model to generalize to any point in the spatial domain, demonstrating models' ability to learn the underlying mechanisms or physics laws of the systems, rather than simply fit the signals. Besides, in temporal domains, \textit{irregularly-sampled} time series, e.g. data with missing values, urge models to be temporally-continuous. Motivated by the two issues, we propose a spatio-temporal framework based on neural operators for PDEs, which learn the underlying mechanisms governing the dynamics of spatially-continuous physical quantities. Experiments show our model's improved performance on forecasting spatially-continuous physic quantities, and its superior generalization to unseen spatial points and ability to handle temporally-irregular data.  ( 2 min )

We propose an analog implementation of the transcendental activation function leveraging two spin-orbit torque magnetoresistive random-access memory (SOT-MRAM) devices and a CMOS inverter. The proposed analog neuron circuit consumes 1.8-27x less power, and occupies 2.5-4931x smaller area, compared to the state-of-the-art analog and digital implementations. Moreover, the developed neuron can be readily integrated with memristive crossbars without requiring any intermediate signal conversion units. The architecture-level analyses show that a fully-analog in-memory computing (IMC) circuit that use our SOT-MRAM neuron along with an SOT-MRAM based crossbar can achieve more than 1.1x, 12x, and 13.3x reduction in power, latency, and energy, respectively, compared to a mixed-signal implementation with analog memristive crossbars and digital neurons. Finally, through cross-layer analyses, we provide a guide on how varying the device-level parameters in our neuron can affect the accuracy of multilayer perceptron (MLP) for MNIST classification.  ( 2 min )

We introduce a scattering covariance matrix which provides non-Gaussian models of time-series having stationary increments. A complex wavelet transform computes signal variations at each scale. Dependencies across scales are captured by the joint covariance across time and scales of complex wavelet coefficients and their modulus. This covariance is nearly diagonalized by a second wavelet transform, which defines the scattering covariance. We show that this set of moments characterizes a wide range of non-Gaussian properties of multi-scale processes. This is analyzed for a variety of processes, including fractional Brownian motions, Poisson, multifractal random walks and Hawkes processes. We prove that self-similar processes have a scattering covariance matrix which is scale invariant. This property can be estimated numerically and defines a class of wide-sense self-similar processes. We build maximum entropy models conditioned by scattering covariance coefficients, and generate new time-series with a microcanonical sampling algorithm. Applications are shown for highly non-Gaussian financial and turbulence time-series.  ( 2 min )

Machine Learning (ML) techniques are increasingly adopted to tackle ever-evolving high-profile network attacks, including DDoS, botnet, and ransomware, due to their unique ability to extract complex patterns hidden in data streams. These approaches are however routinely validated with data collected in the same environment, and their performance degrades when deployed in different network topologies and/or applied on previously unseen traffic, as we uncover. This suggests malicious/benign behaviors are largely learned superficially and ML-based Network Intrusion Detection System (NIDS) need revisiting, to be effective in practice. In this paper we dive into the mechanics of large-scale network attacks, with a view to understanding how to use ML for Network Intrusion Detection (NID) in a principled way. We reveal that, although cyberattacks vary significantly in terms of payloads, vectors and targets, their early stages, which are critical to successful attack outcomes, share many similarities and exhibit important temporal correlations. Therefore, we treat NID as a time-sensitive task and propose NetSentry, perhaps the first of its kind NIDS that builds on Bidirectional Asymmetric LSTM (Bi-ALSTM), an original ensemble of sequential neural models, to detect network threats before they spread. We cross-evaluate NetSentry using two practical datasets, training on one and testing on the other, and demonstrate F1 score gains above 33% over the state-of-the-art, as well as up to 3 times higher rates of detecting attacks such as XSS and web bruteforce. Further, we put forward a novel data augmentation technique that boosts the generalization abilities of a broad range of supervised deep learning algorithms, leading to average F1 score gains above 35%.  ( 2 min )

Infographics are an aesthetic visual representation of information following specific design principles of human perception. Designing infographics can be a tedious process for non-experts and time-consuming, even for professional designers. With the help of designers, we propose a semi-automated infographic framework for general structured and flow-based infographic design generation. For novice designers, our framework automatically creates and ranks infographic designs for a user-provided text with no requirement for design input. However, expert designers can still provide custom design inputs to customize the infographics. We will also contribute an individual visual group (VG) designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs in this work. Evaluation results confirm that by using our framework, designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining the same quality as hand-designed infographics templates.  ( 2 min )

Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network (GNN), in which each node's representation is computed recursively by aggregating representations (messages) from its immediate neighbors akin to a star-shaped pattern. MPNNs are appealing for being efficient and scalable, how-ever their expressiveness is upper-bounded by the 1st-order Weisfeiler-Lehman isomorphism test (1-WL). In response, prior works propose highly expressive models at the cost of scalability and sometimes generalization performance. Our work stands between these two regimes: we introduce a general framework to uplift any MPNN to be more expressive, with limited scalability overhead and greatly improved practical performance. We achieve this by extending local aggregation in MPNNs from star patterns to general subgraph patterns (e.g.,k-egonets):in our framework, each node representation is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only (i.e. a star). We choose the subgraph encoder to be a GNN (mainly MPNNs, considering scalability) to design a general framework that serves as a wrapper to up-lift any GNN. We call our proposed method GNN-AK(GNN As Kernel), as the framework resembles a convolutional neural network by replacing the kernel with GNNs. Theoretically, we show that our framework is strictly more powerful than 1&2-WL, and is not less powerful than 3-WL. We also design subgraph sampling strategies which greatly reduce memory footprint and improve speed while maintaining performance. Our method sets new state-of-the-art performance by large margins for several well-known graph ML tasks; specifically, 0.08 MAE on ZINC,74.79% and 86.887% accuracy on CIFAR10 and PATTERN respectively.  ( 2 min )

The task of continual learning requires careful design of algorithms that can tackle catastrophic forgetting. However, the noisy label, which is inevitable in a real-world scenario, seems to exacerbate the situation. While very few studies have addressed the issue of continual learning under noisy labels, long training time and complicated training schemes limit their applications in most cases. In contrast, we propose a simple purification technique to effectively cleanse the online data stream that is both cost-effective and more accurate. After purification, we perform fine-tuning in a semi-supervised fashion that ensures the participation of all available samples. Training in this fashion helps us learn a better representation that results in state-of-the-art (SOTA) performance. Through extensive experimentation on 3 benchmark datasets, MNIST, CIFAR10 and CIFAR100, we show the effectiveness of our proposed approach. We achieve a 24.8% performance gain for CIFAR10 with 20% noise over previous SOTA methods. Our code is publicly available.  ( 2 min )

Email threat is a serious issue for enterprise security, which consists of various malicious scenarios, such as phishing, fraud, blackmail and malvertisement. Traditional anti-spam gateway commonly requires to maintain a greylist to filter out unexpected emails based on suspicious vocabularies existed in the mail subject and content. However, the signature-based approach cannot effectively discover novel and unknown suspicious emails that utilize various hot topics at present, such as COVID-19 and US election. To address the problem, in this paper, we present Holmes, an efficient and lightweight semantic based engine for anomalous email detection. Holmes can convert each event log of email to a sentence through word embedding then extract interesting items among them by novelty detection. Based on our observations, we claim that, in an enterprise environment, there is a stable relation between senders and receivers, but suspicious emails are commonly from unusual sources, which can be detected through the rareness selection. We evaluate the performance of Holmes in a real-world enterprise environment, in which it sends and receives around 5,000 emails each day. As a result, Holmes can achieve a high detection rate (output around 200 suspicious emails per day) and maintain a low false alarm rate for anomaly detection.  ( 3 min )

In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings. We also incorporate temporal modeling to put this additional interpretability to action by parsing out topic similarities as a time series in a turn-level resolution. We believe this topic modeling framework can offer interpretable insights for the therapist to optimally decide his or her strategy and improve the psychotherapy effectiveness.  ( 2 min )

Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks. However, the current theoretical understanding of self-training only applies to linear models. This work provides a unified theoretical analysis of self-training with deep networks for semi-supervised learning, unsupervised domain adaptation, and unsupervised learning. At the core of our analysis is a simple but realistic "expansion" assumption, which states that a low probability subset of the data must expand to a neighborhood with large probability relative to the subset. We also assume that neighborhoods of examples in different classes have minimal overlap. We prove that under these assumptions, the minimizers of population objectives based on self-training and input-consistency regularization will achieve high accuracy with respect to ground-truth labels. By using off-the-shelf generalization bounds, we immediately convert this result to sample complexity guarantees for neural nets that are polynomial in the margin and Lipschitzness. Our results help explain the empirical successes of recently proposed self-training algorithms which use input consistency regularization.  ( 2 min )

Safe deployment of deep learning systems in critical real world applications requires models to make very few mistakes, and only under predictable circumstances. In this work, we address this problem using an abstaining classifier that is tuned to have $>$95% accuracy, and then identify the determinants of abstention using LIME. Essentially, we are training our model to learn the attributes of pathology reports that are likely to lead to incorrect classifications, albeit at the cost of reduced sensitivity. We demonstrate an abstaining classifier in a multitask setting for classifying cancer pathology reports from the NCI SEER cancer registries on six tasks of interest. For these tasks, we reduce the classification error rate by factors of 2--5 by abstaining on 25--45% of the reports. For the specific task of classifying cancer site, we are able to identify metastasis, reports involving lymph nodes, and discussion of multiple cancer sites as responsible for many of the classification mistakes, and observe that the extent and types of mistakes vary systematically with cancer site (e.g., breast, lung, and prostate). When combining across three of the tasks, our model classifies 50% of the reports with an accuracy greater than 95% for three of the six tasks\edit, and greater than 85% for all six tasks on the retained samples. Furthermore, we show that LIME provides a better determinant of classification than measures of word occurrence alone. By combining a deep abstaining classifier with feature identification using LIME, we are able to identify concepts responsible for both correctness and abstention when classifying cancer sites from pathology reports. The improvement of LIME over keyword searches is statistically significant, presumably because words are assessed in context and have been identified as a local determinant of classification.  ( 3 min )

Sequential Recommendation aims to predict the next item based on user behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to improve recommendation performance. However, most of existing SSL methods use a uniform data augmentation scheme, which loses the sequence correlation of an original sequence. To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec). Specifically, LMA4Rec first takes model augmentation as a supplementary method for data augmentation to generate views. Then, LMA4Rec uses learnable Bernoulli dropout to implement model augmentation learnable operations. Next, self-supervised learning is used between the contrastive views to extract self-supervised signals from an original sequence. Finally, experiments on three public datasets show that the LMA4Rec method effectively improves sequential recommendation performance compared with baseline methods.  ( 2 min )

Deep learning on point clouds has received increased attention thanks to its wide applications in AR/VR and autonomous driving. These applications require low latency and high accuracy to provide real-time user experience and ensure user safety. Unlike conventional dense workloads, the sparse and irregular nature of point clouds poses severe challenges to running sparse CNNs efficiently on the general-purpose hardware. Furthermore, existing sparse acceleration techniques for 2D images do not translate to 3D point clouds. In this paper, we introduce TorchSparse, a high-performance point cloud inference engine that accelerates the sparse convolution computation on GPUs. TorchSparse directly optimizes the two bottlenecks of sparse convolution: irregular computation and data movement. It applies adaptive matrix multiplication grouping to trade computation for better regularity, achieving 1.4-1.5x speedup for matrix multiplication. It also optimizes the data movement by adopting vectorized, quantized and fused locality-aware memory access, reducing the memory movement cost by 2.7x. Evaluated on seven representative models across three benchmark datasets, TorchSparse achieves 1.6x and 1.5x measured end-to-end speedup over the state-of-the-art MinkowskiEngine and SpConv, respectively.  ( 2 min )

We explore future object prediction -- a challenging problem where all objects visible in a future video frame are to be predicted. We propose to tackle this problem end-to-end by training a detection transformer to directly output future objects. In order to make accurate predictions about the future, it is necessary to capture the dynamics in the scene, both of other objects and of the ego-camera. We extend existing detection transformers in two ways to capture the scene dynamics. First, we experiment with three different mechanisms that enable the model to spatiotemporally process multiple frames. Second, we feed ego-motion information to the model via cross-attention. We show that both of these cues substantially improve future object prediction performance. Our final approach learns to capture the dynamics and make predictions on par with an oracle for 100 ms prediction horizons, and outperform baselines for longer prediction horizons.  ( 2 min )

The great success of deep neural networks is built upon their over-parameterization, which smooths the optimization landscape without degrading the generalization ability. Despite the benefits of over-parameterization, a huge amount of parameters makes deep networks cumbersome in daily life applications. Though techniques such as pruning and distillation are developed, they are expensive in fully training a dense network as backward selection methods, and there is still a void on systematically exploring forward selection methods for learning structural sparsity in deep networks. To fill in this gap, this paper proposes a new approach based on differential inclusions of inverse scale spaces, which generate a family of models from simple to complex ones along the dynamics via coupling a pair of parameters, such that over-parameterized deep models and their structural sparsity can be explored simultaneously. This kind of differential inclusion scheme has a simple discretization, dubbed Deep structure splitting Linearized Bregman Iteration (DessiLBI), whose global convergence in learning deep networks could be established under the Kurdyka-Lojasiewicz framework. Experimental evidence shows that our method achieves comparable and even better performance than the competitive optimizers in exploring the sparse structure of several widely used backbones on the benchmark datasets. Remarkably, with early stopping, our method unveils `winning tickets' in early epochs: the effective sparse network structures with comparable test accuracy to fully trained over-parameterized models, that are further transferable to similar alternative tasks. Furthermore, our method is able to grow networks efficiently with adaptive filter configurations, demonstrating a good performance with much less computational cost. Codes and models can be downloaded at {https://github.com/DessiLBI2020/DessiLBI}.  ( 3 min )

The majority of IoT devices like smartwatches, smart plugs, HVAC controllers, etc., are powered by hardware with a constrained specification (low memory, clock speed and processor) which is insufficient to accommodate and execute large, high-quality models. On such resource-constrained devices, manufacturers still manage to provide attractive functionalities (to boost sales) by following the traditional approach of programming IoT devices/products to collect and transmit data (image, audio, sensor readings, etc.) to their cloud-based ML analytics platforms. For decades, this online approach has been facing issues such as compromised data streams, non-real-time analytics due to latency, bandwidth constraints, costly subscriptions, recent privacy issues raised by users and the GDPR guidelines, etc. In this paper, to enable ultra-fast and accurate AI-based offline analytics on resource-constrained IoT devices, we present an end-to-end multi-component model optimization sequence and open-source its implementation. Researchers and developers can use our optimization sequence to optimize high memory, computation demanding models in multiple aspects in order to produce small size, low latency, low-power consuming models that can comfortably fit and execute on resource-constrained hardware. The experimental results show that our optimization components can produce models that are; (i) 12.06 x times compressed; (ii) 0.13% to 0.27% more accurate; (iii) Orders of magnitude faster unit inference at 0.06 ms. Our optimization sequence is generic and can be applied to any state-of-the-art models trained for anomaly detection, predictive maintenance, robotics, voice recognition, and machine vision.  ( 2 min )

Motivated by the growing importance of reducing unfairness in ML predictions, Fair-ML researchers have presented an extensive suite of algorithmic "fairness-enhancing" remedies. Most existing algorithms, however, are agnostic to the sources of the observed unfairness. As a result, the literature currently lacks guiding frameworks to specify conditions under which each algorithmic intervention can potentially alleviate the underpinning cause of unfairness. To close this gap, we scrutinize the underlying biases (e.g., in the training data or design choices) that cause observational unfairness. We present a bias-injection sandbox tool to investigate fairness consequences of various biases and assess the effectiveness of algorithmic remedies in the presence of specific types of bias. We call this process the bias(stress)-testing of algorithmic interventions. Unlike existing toolkits, ours provides a controlled environment to counterfactually inject biases in the ML pipeline. This stylized setup offers the distinct capability of testing fairness interventions beyond observational data and against an unbiased benchmark. In particular, we can test whether a given remedy can alleviate the injected bias by comparing the predictions resulting after the intervention in the biased setting with true labels in the unbiased regime -- that is, before any bias injection. We illustrate the utility of our toolkit via a proof-of-concept case study on synthetic data. Our empirical analysis showcases the type of insights that can be obtained through our simulations.  ( 2 min )

Anomaly detection is important for industrial automation and part quality assurance, and while humans can easily detect anomalies in components given a few examples, designing a generic automated system that can perform at human or above human capabilities remains a challenge. In this work, we present a simple new anomaly detection algorithm called FADS (feature-based anomaly detection system) which leverages pretrained convolutional neural networks (CNN) to generate a statistical model of nominal inputs by observing the activation of the convolutional filters. During inference the system compares the convolutional filter activation of the new input to the statistical model and flags activations that are outside the expected range of values and therefore likely an anomaly. By using a pretrained network, FADS demonstrates excellent performance similar to or better than other machine learning approaches to anomaly detection while at the same time FADS requires no tuning of the CNN weights. We demonstrate FADS ability by detecting process parameter changes on a custom dataset of additively manufactured lattices. The FADS localization algorithm shows that textural differences that are visible on the surface can be used to detect process parameter changes. In addition, we test FADS on benchmark datasets, such as the MVTec Anomaly Detection dataset, and report good results.  ( 2 min )

Predicting incoming failures and scheduling maintenance based on sensors information in industrial machines is increasingly important to avoid downtime and machine failure. Different machine learning formulations can be used to solve the predictive maintenance problem. However, many of the approaches studied in the literature are not directly applicable to real-life scenarios. Indeed, many of those approaches usually either rely on labelled machine malfunctions in the case of classification and fault detection, or rely on finding a monotonic health indicator on which a prediction can be made in the case of regression and remaining useful life estimation, which is not always feasible. Moreover, the decision-making part of the problem is not always studied in conjunction with the prediction phase. This paper aims to design and compare different formulations for predictive maintenance in a two-level framework and design metrics that quantify both the failure detection performance as well as the timing of the maintenance decision. The first level is responsible for building a health indicator by aggregating features using a learning algorithm. The second level consists of a decision-making system that can trigger an alarm based on this health indicator. Three degrees of refinements are compared in the first level of the framework, from simple threshold-based univariate predictive technique to supervised learning methods based on the remaining time before failure. We choose to use the Support Vector Machine (SVM) and its variations as the common algorithm used in all the formulations. We apply and compare the different strategies on a real-world rotating machine case study and observe that while a simple model can already perform well, more sophisticated refinements enhance the predictions for well-chosen parameters.  ( 2 min )

Urban traffic flow prediction using data-driven models can play an important role in route planning and preventing congestion on highways. These methods utilize data collected from traffic recording stations at different timestamps to predict the future status of traffic. Hence, data collection, transmission, storage, and extraction techniques can have a significant impact on the performance of the traffic flow model. On the other hand, a comprehensive database can provide the opportunity for using complex, yet reliable predictive models such as deep learning methods. However, most of these methods have difficulties in handling missing values and outliers. This study focuses on hybrid deep neural networks to predict traffic flow in the California Freeway Performance Measurement System (PeMS) with missing values. The proposed networks are based on a combination of recurrent neural networks (RNNs) to consider the temporal dependencies in the data recorded in each station and convolutional neural networks (CNNs) to take the spatial correlations in the adjacent stations into account. Various architecture configurations with series and parallel connections are considered based on RNNs and CNNs, and several prevalent data imputation techniques are used to examine the robustness of the hybrid networks to missing values. A comprehensive analysis performed on two different datasets from PeMS indicates that the proposed series-parallel hybrid network with the mean imputation technique achieves the lowest error in predicting the traffic flow and is robust to missing values up until 21% missing ratio in both complete and incomplete training data scenarios when applied to an incomplete test data.  ( 2 min )

Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4,360 IVOCT image frames of 77 lesions among 41 patients. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, theta) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7+/-17 degree; mean 196 degree). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2+/-14.6 micron; mean 175 micron), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.  ( 2 min )

The US National Institute of Standards and Technology (NIST) has been conducting a second iteration of the CTS challenge since August 2020. The current iteration of the CTS Challenge is a leaderboard-style speaker recognition evaluation using telephony data extracted from the unexposed portions of the Call My Net 2 (CMN2) and Multi-Language Speech (MLS) corpora collected by the LDC. The CTS Challenge is currently organized in a similar manner to the SRE19 CTS Challenge, offering only an open training condition using two evaluation subsets, namely Progress and Test. Unlike in the SRE19 Challenge, no training or development set was initially released, and NIST has publicly released the leaderboards on both subsets for the CTS Challenge. Which subset (i.e., Progress or Test) a trial belongs to is unknown to challenge participants, and each system submission needs to contain outputs for all of the trials. The CTS Challenge has also served, and will continue to do so, as a prerequisite for entrance to the regular SREs (such as SRE21). Since August 2020, a total of 53 organizations (forming 33 teams) from academia and industry have participated in the CTS Challenge and submitted more than 4400 valid system outputs. This paper presents an overview of the evaluation and several analyses of system performance for some primary conditions in the CTS Challenge. The CTS Challenge results thus far indicate remarkable improvements in performance due to 1) speaker embeddings extracted using large-scale and complex neural network architectures such as ResNets along with angular margin losses for speaker embedding extraction, 2) extensive data augmentation, 3) the use of large amounts of in-house proprietary data from a large number of labeled speakers, 4) long-duration fine-tuning.  ( 2 min )

Actor-critic algorithms that make use of distributional policy evaluation have frequently been shown to outperform their non-distributional counterparts on many challenging control tasks. Examples of this behavior include the D4PG and DMPO algorithms as compared to DDPG and MPO, respectively [Barth-Maron et al., 2018; Hoffman et al., 2020]. However, both agents rely on the C51 critic for value estimation.One major drawback of the C51 approach is its requirement of prior knowledge about the minimum andmaximum values a policy can attain as well as the number of bins used, which fixes the resolution ofthe distributional estimate. While the DeepMind control suite of tasks utilizes standardized rewards and episode lengths, thus enabling the entire suite to be solved with a single setting of these hyperparameters, this is often not the case. This paper revisits a natural alternative that removes this requirement, namelya mixture of Gaussians, and a simple sample-based loss function to train it in an off-policy regime. We empirically evaluate its performance on a broad range of continuous control tasks and demonstrate that it eliminates the need for these distributional hyperparameters and achieves state-of-the-art performance on a variety of challenging tasks (e.g. the humanoid, dog, quadruped, and manipulator domains). Finallywe provide an implementation in the Acme agent repository.  ( 2 min )

In recent years, there has been a lot of focus on offensive content. The amount of offensive content generated by social media is increasing at an alarming rate. This created a greater need to address this issue than ever before. To address these issues, the organizers of "Dravidian-Code Mixed HASOC-2020" have created two challenges. Task 1 involves identifying offensive content in Malayalam data, whereas Task 2 includes Malayalam and Tamil Code Mixed Sentences. Our team participated in Task 2. In our suggested model, we experiment with multilingual BERT to extract features, and three different classifiers are used on extracted features. Our model received a weighted F1 score of 0.70 for Malayalam data and was ranked fifth; we also received a weighted F1 score of 0.573 for Tamil Code Mixed data and were ranked eleventh.  ( 2 min )

Extracting phenotypes from clinical text has been shown to be useful for a variety of clinical use cases such as identifying patients with rare diseases. However, reasoning with numerical values remains challenging for phenotyping in clinical text, for example, temperature 102F representing Fever. Current state-of-the-art phenotyping models are able to detect general phenotypes, but perform poorly when they detect phenotypes requiring numerical reasoning. We present a novel unsupervised methodology leveraging external knowledge and contextualized word embeddings from ClinicalBERT for numerical reasoning in a variety of phenotypic contexts. Comparing against unsupervised benchmarks, it shows a substantial performance improvement with absolute gains on generalized Recall and F1 scores up to 79% and 71%, respectively. In the supervised setting, it also surpasses the performance of alternative approaches with absolute gains on generalized Recall and F1 scores up to 70% and 44%, respectively.  ( 2 min )

The growing use of deep neural networks (DNNs) in safety- and security-critical areas like autonomous driving raises the need for their systematic testing. Coverage-guided testing (CGT) is an approach that applies mutation or fuzzing according to a predefined coverage metric to find inputs that cause misbehavior. With the introduction of a neuron coverage metric, CGT has also recently been applied to DNNs. In this work, we apply CGT to the task of person detection in crowded scenes. The proposed pipeline uses YOLOv3 for person detection and includes finding DNN bugs via sampling and mutation, and subsequent DNN retraining on the updated training set. To be a bug, we require a mutated image to cause a significant performance drop compared to a clean input. In accordance with the CGT, we also consider an additional requirement of increased coverage in the bug definition. In order to explore several types of robustness, our approach includes natural image transformations, corruptions, and adversarial examples generated with the Daedalus attack. The proposed framework has uncovered several thousand cases of incorrect DNN behavior. The relative change in mAP performance of the retrained models reached on average between 26.21\% and 64.24\% for different robustness types. However, we have found no evidence that the investigated coverage metrics can be advantageously used to improve robustness.  ( 2 min )

Here I present an investigation on the evolution and use of vocabulary in data science in the last 13 years. Based on a rigorous statistical analysis, a database with 12,787 documents containing the words "data science" in the title, abstract or keywords is analyzed. It is proposed to classify the evolution of this discipline in three periods: emergence, growth and boom. Characteristic words and pioneering documents are identified for each period. By proposing the distinctive vocabulary and relevant topics of data science and classified in time periods, these results add value to the scientific community of this discipline.  ( 2 min )

Graph neural networks (GNNs) have been widely used in many real applications, and recent studies have revealed their vulnerabilities against topology attacks. To address this issue, existing efforts have mainly been dedicated to improving the robustness of GNNs, while little attention has been paid to the detection of such attacks. In this work, we study the victim node detection problem under topology attacks against GNNs. Our approach is built upon the key observation rooted in the intrinsic message passing nature of GNNs. That is, the neighborhood of a victim node tends to have two competing group forces, pushing the node classification results towards the original label and the targeted label, respectively. Based on this observation, we propose to detect victim nodes by deliberately designing an effective measurement of the neighborhood variance for each node. Extensive experimental results on four real-world datasets and five existing topology attacks show the effectiveness and efficiency of the proposed detection approach.  ( 2 min )

Video decomposition is very important to extract moving foreground objects from complex backgrounds in computer vision, machine learning, and medical imaging, e.g., extracting moving contrast-filled vessels from the complex and noisy backgrounds of X-ray coronary angiography (XCA). However, the challenges caused by dynamic backgrounds, overlapping heterogeneous environments and complex noises still exist in video decomposition. To solve these problems, this study is the first to introduce a flexible visual working memory model in video decomposition tasks to provide interpretable and high-performance hierarchical deep architecture, integrating the transformative representations between sensory and control layers from the perspective of visual and cognitive neuroscience. Specifically, robust PCA unrolling networks acting as a structure-regularized sensor layer decompose XCA into sparse/low-rank structured representations to separate moving contrast-filled vessels from noisy and complex backgrounds. Then, patch recurrent convolutional LSTM networks with a backprojection module embody unstructured random representations of the control layer in working memory, recurrently projecting spatiotemporally decomposed nonlocal patches into orthogonal subspaces for heterogeneous vessel retrieval and interference suppression. This video decomposition deep architecture effectively restores the heterogeneous profiles of intensity and the geometries of moving objects against the complex background interferences. Experiments show that the proposed method significantly outperforms state-of-the-art methods in accurate moving contrast-filled vessel extraction with excellent flexibility and computational efficiency.  ( 2 min )

Correctly quantifying the robustness of machine learning models is a central aspect in judging their suitability for specific tasks, and thus, ultimately, for generating trust in the models. We show that the widely used concept of adversarial robustness and closely related metrics based on counterfactuals are not necessarily valid metrics for determining the robustness of ML models against perturbations that occur "naturally", outside specific adversarial attack scenarios. Additionally, we argue that generic robustness metrics in principle are insufficient for determining real-world-robustness. Instead we propose a flexible approach that models possible perturbations in input data individually for each application. This is then combined with a probabilistic approach that computes the likelihood that a real-world perturbation will change a prediction, thus giving quantitative information of the robustness of the trained machine learning model. The method does not require access to the internals of the classifier and thus in principle works for any black-box model. It is, however, based on Monte-Carlo sampling and thus only suited for input spaces with small dimensions. We illustrate our approach on two dataset, as well as on analytically solvable cases. Finally, we discuss ideas on how real-world robustness could be computed or estimated in high-dimensional input spaces.  ( 2 min )

Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are susceptible to a mechanism called fluctuation. This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing any distance, density or isolation measure. Fundamentally different from all existing methods. FBOD first converts the Euclidean structure datasets into graphs by using random links, then propagates the feature value according to the connection of the graph. Finally, by comparing the difference between the fluctuation of an object and its neighbors, FBOD determines the object with a larger difference as an outlier. The results of experiments comparing FBOD with seven state-of-the-art algorithms on eight real-world tabular datasets and three video datasets show that FBOD outperforms its competitors in the majority of cases and that FBOD has only 5% of the execution time of the fastest algorithm. The experiment codes are available at: https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection.  ( 2 min )

An end-to-end platform assembling multiple tiers is built for precisely cognizing brain activities. Being fed massive electroencephalogram (EEG) data, the time-frequency spectrograms are conventionally projected into the episode-wise feature matrices (seen as tier-1). A spiking neural network (SNN) based tier is designed to distill the principle information in terms of spike-streams from the rare features, which maintains the temporal implication in the nature of EEGs. The proposed tier-3 transposes time- and space-domain of spike patterns from the SNN; and feeds the transposed pattern-matrices into an artificial neural network (ANN, Transformer specifically) known as tier-4, where a special spanning topology is proposed to match the two-dimensional input form. In this manner, cognition such as classification is conducted with high accuracy. For proof-of-concept, the sleep stage scoring problem is demonstrated by introducing multiple EEG datasets with the largest comprising 42,560 hours recorded from 5,793 subjects. From experiment results, our platform achieves the general cognition overall accuracy of 87% by leveraging sole EEG, which is 2% superior to the state-of-the-art. Moreover, our developed multi-tier methodology offers visible and graphical interpretations of the temporal characteristics of EEG by identifying the critical episodes, which is demanded in neurodynamics but hardly appears in conventional cognition scenarios.  ( 2 min )

In the process of collecting data from sensors, several circumstances can affect their continuity and validity, resulting in alterations of the data or loss of information. Although classical methods of statistics, such as interpolation-like techniques, can be used to approximate the missing data in a time series, the recent developments in Deep Learning (DL) have given impetus to innovative and much more accurate forecasting techniques. In the present paper, we develop two DL models aimed at filling data gaps, for the specific case of internal temperature time series obtained from monitored apartments located in Bolzano, Italy. The DL models developed in the present work are based on the combination of Convolutional Neural Networks (CNNs), Long Short-Term Memory Neural Networks (LSTMs), and Bidirectional LSTMs (BiLSTMs). Two key features of our models are the use of both pre- and post-gap data, and the exploitation of a correlated time series (the external temperature) in order to predict the target one (the internal temperature). Our approach manages to capture the fluctuating nature of the data and shows good accuracy in reconstructing the target time series. In addition, our models significantly improve the already good results from another DL architecture that is used as a baseline for the present work.  ( 2 min )

Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index has been explored actively to replace or supplement traditional index structures with machine learning models to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, a novel indexing approach called LIMS is proposed to use data clustering and pivot-based data transformation techniques to build learned indexes for efficient similarity query processing in metric spaces. The underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on the disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.  ( 2 min )

Gaussian processes (GPs) are powerful but computationally expensive machine learning models, requiring an estimate of the kernel covariance matrix for every prediction. In large and complex domains, such as graphs, sets, or images, the choice of suitable kernel can also be non-trivial to determine, providing an additional obstacle to the learning task. Over the last decade, these challenges have resulted in significant advances being made in terms of scalability and expressivity, exemplified by, e.g., the use of inducing points and neural network kernel approximations. In this paper, we propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains while also facilitating scalable gradient-based learning methods. We consider both regression and (binary) classification tasks and report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods. We also demonstrate how IGNs can be used to effectively model complex domains using neural network architectures.  ( 2 min )

Traditional machine learning algorithms are designed to learn in isolation, i.e. address single tasks. The core idea of transfer learning (TL) is that knowledge gained in learning to perform one task (source) can be leveraged to improve learning performance in a related, but different, task (target). TL leverages and transfers previously acquired knowledge to address the expense of data acquisition and labeling, potential computational power limitations, and the dataset distribution mismatches. Although significant progress has been made in the fields of image processing, speech recognition, and natural language processing (for classification and regression) for TL, little work has been done in the field of scientific machine learning for functional regression and uncertainty quantification in partial differential equations. In this work, we propose a novel TL framework for task-specific learning under conditional shift with a deep operator network (DeepONet). Inspired by the conditional embedding operator theory, we measure the statistical distance between the source domain and the target feature domain by embedding conditional distributions onto a reproducing kernel Hilbert space. Task-specific operator learning is accomplished by fine-tuning task-specific layers of the target DeepONet using a hybrid loss function that allows for the matching of individual target samples while also preserving the global properties of the conditional distribution of target data. We demonstrate the advantages of our approach for various TL scenarios involving nonlinear PDEs under conditional shift. Our results include geometry domain adaptation and show that the proposed TL framework enables fast and efficient multi-task operator learning, despite significant differences between the source and target domains.  ( 2 min )

Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we summarize the widely used datasets in this emerging research field and provide insights on current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances.  ( 2 min )

Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter. The development of marginal feature importance methods, such as marginal contribution feature importance, attempts to break this trend by providing a useful framework for explaining relationships in data in an interpretable fashion. In this work, we generalize the framework of marginal contribution feature importance to improve performance with regards to detecting correlated interactions and reducing runtime. To do so, we consider "information subsets" of the set of features $F$ and show that our importance metric can be computed directly after applying fair representation learning methods from the AI fairness literature. The methods of optimal transport and linear regression are considered and explored experimentally for removing all the information of our feature of interest $f$ from the feature set $F$. Given these implementations, we show on real and simulated data that ultra marginal feature importance performs at least as well as marginal contribution feature importance, with substantially faster computation time and better performance in the presence of correlated interactions and unrelated features.  ( 2 min )

Artificial intelligence (AI) systems power the world we live in. Deep neural networks (DNNs) are able to solve tasks in an ever-expanding landscape of scenarios, but our eagerness to apply these powerful models leads us to focus on their performance and deprioritises our ability to understand them. Current research in the field of explainable AI tries to bridge this gap by developing various perturbation or gradient-based explanation techniques. For images, these techniques fail to fully capture and convey the semantic information needed to elucidate why the model makes the predictions it does. In this work, we develop a new form of explanation that is radically different in nature from current explanation methods, such as Grad-CAM. Perception visualization provides a visual representation of what the DNN perceives in the input image by depicting what visual patterns the latent representation corresponds to. Visualizations are obtained through a reconstruction model that inverts the encoded features, such that the parameters and predictions of the original models are not modified. Results of our user study demonstrate that humans can better understand and predict the system's decisions when perception visualizations are available, thus easing the debugging and deployment of deep models as trusted systems.  ( 2 min )

Recently, graph convolutional networks (GCNs) have shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks. However, current approaches for defense are typically designed for the whole graph and consider the global performance, posing challenges in protecting important local nodes from stronger adversarial targeted attacks. In this work, we present a simple yet effective method, named \textbf{\underline{G}}raph \textbf{\underline{U}}niversal \textbf{\underline{A}}dve\textbf{\underline{R}}sarial \textbf{\underline{D}}efense (GUARD). Unlike previous works, GUARD protects each individual node from attacks with a universal defensive patch, which is generated once and can be applied to any node (node-agnostic) in a graph. Extensive experiments on four benchmark datasets demonstrate that our method significantly improves robustness for several established GCNs against multiple adversarial attacks and outperforms existing adversarial defense methods by large margins. Our code is publicly available at https://github.com/EdisonLeeeee/GUARD.  ( 2 min )

In healthcare prediction tasks, it is essential to exploit the correlations between medical features and learn better patient health representations. Existing methods try to estimate feature correlations only from data, or increase the quality of estimation by introducing task-specific medical knowledge. However, such methods either are difficult to estimate the feature correlations due to insufficient training samples, or cannot be generalized to other tasks due to reliance on specific knowledge. There are medical research revealing that not all the medical features are strongly correlated. Thus, to address the issues, we expect to group up strongly correlated features and learn feature correlations in a group-wise manner to reduce the learning complexity without losing generality. In this paper, we propose a general patient health representation learning framework MedFACT. We estimate correlations via measuring similarity between temporal patterns of medical features with kernel methods, and cluster features with strong correlations into groups. The feature group is further formulated as a correlation graph, and we employ graph convolutional networks to conduct group-wise feature interactions for better representation learning. Experiments on two real-world datasets demonstrate the superiority of MedFACT. The discovered medical findings are also confirmed by literature, providing valuable medical insights and explanations.  ( 2 min )

Extracting actionable information from data sources such as the Linac Coherent Light Source (LCLS-II) and Advanced Photon Source Upgrade (APS-U) is becoming more challenging due to the fast-growing data generation rate. The rapid analysis possible with ML methods can enable fast feedback loops that can be used to adjust experimental setups in real-time, for example when errors occur or interesting events are detected. However, to avoid degradation in ML performance over time due to changes in an instrument or sample, we need a way to update ML models rapidly while an experiment is running. We present here a data service and model service to accelerate deep neural network training with a focus on ML-based scientific applications. Our proposed data service achieves 100x speedup in terms of data labeling compare to the current state-of-the-art. Further, our model service achieves up to 200x improvement in training speed. Overall, fairDMS achieves up to 92x speedup in terms of end-to-end model updating time.  ( 2 min )

Despite the success of reinforcement learning (RL) for Markov decision processes (MDPs) with function approximation, most RL algorithms easily fail if the agent only has partial observations of the state. Such a setting is often modeled as a partially observable Markov decision process (POMDP). Existing sample-efficient algorithms for POMDPs are restricted to the tabular setting where the state and observation spaces are finite. In this paper, we make the first attempt at tackling the tension between function approximation and partial observability. In specific, we focus on a class of undercomplete POMDPs with linear function approximations, which allows the state and observation spaces to be infinite. For such POMDPs, we show that the optimal policy and value function can be characterized by a sequence of finite-memory Bellman operators. We propose an RL algorithm that constructs optimistic estimators of these operators via reproducing kernel Hilbert space (RKHS) embedding. Moreover, we theoretically prove that the proposed algorithm finds an $\varepsilon$-optimal policy with $\tilde O (1/\varepsilon^2)$ episodes of exploration. Also, this sample complexity only depends on the intrinsic dimension of the POMDP polynomially and is independent of the size of the state and observation spaces. To our best knowledge, we develop the first provably sample-efficient algorithm for POMDPs with function approximation.  ( 2 min )

Most studies in cross-device federated learning focus on small models, due to the server-client communication and on-device computation bottlenecks. In this work, we leverage various techniques for mitigating these bottlenecks to train larger language models in cross-device federated learning. With systematic applications of partial model training, quantization, efficient transfer learning, and communication-efficient optimizers, we are able to train a $21$M parameter Transformer that achieves the same perplexity as that of a similarly sized LSTM with $\sim10\times$ smaller client-to-server communication cost and $11\%$ lower perplexity than smaller LSTMs commonly studied in literature.  ( 2 min )

A misspecified reward can degrade sample efficiency and induce undesired behaviors in reinforcement learning (RL) problems. We propose symbolic reward machines for incorporating high-level task knowledge when specifying the reward signals. Symbolic reward machines augment existing reward machine formalism by allowing transitions to carry predicates and symbolic reward outputs. This formalism lends itself well to inverse reinforcement learning, whereby the key challenge is determining appropriate assignments to the symbolic values from a few expert demonstrations. We propose a hierarchical Bayesian approach for inferring the most likely assignments such that the concretized reward machine can discriminate expert demonstrated trajectories from other trajectories with high accuracy. Experimental results show that learned reward machines can significantly improve training efficiency for complex RL tasks and generalize well across different task environment configurations.  ( 2 min )

The limited communication resources, e.g., bandwidth and energy, and data heterogeneity across devices are two of the main bottlenecks for federated learning (FL). To tackle these challenges, we first devise a novel FL framework with partial model aggregation (PMA), which only aggregates the lower layers of neural networks responsible for feature extraction while the upper layers corresponding to complex pattern recognition remain at devices for personalization. The proposed PMA-FL is able to address the data heterogeneity and reduce the transmitted information in wireless channels. We then obtain a convergence bound of the framework under a non-convex loss function setting. With the aid of this bound, we define a new objective function, named the scheduled data sample volume, to transfer the original inexplicit optimization problem into a tractable one for device scheduling, bandwidth allocation, computation and communication time division. Our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the optimal device scheduling. Compared with the state-of-the-art benchmarks, the proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets, i.e., MINIST and CIFAR-10, respectively. In addition, the proposed joint dynamic device scheduling and resource optimization approach achieve slightly higher accuracy than the considered benchmarks, but they provide a satisfactory energy and time reduction: 29% energy or 20% time reduction on the MNIST; and 25% energy or 12.5% time reduction on the CIFAR-10.  ( 2 min )

In this paper, we consider the policy evaluation problem in multi-agent reinforcement learning (MARL) and derive exact closed-form formulas for the finite-time mean-squared estimation errors of decentralized temporal difference (TD) learning with linear function approximation. Our analysis hinges upon the fact that the decentralized TD learning method can be viewed as a Markov jump linear system (MJLS). Then standard MJLS theory can be applied to quantify the mean and covariance matrix of the estimation error of the decentralized TD method at every time step. Various implications of our exact formulas on the algorithm performance are also discussed. An interesting finding is that under a necessary and sufficient stability condition, the mean-squared TD estimation error will converge to an exact limit at a specific exponential rate.  ( 2 min )

Businesses need content. In various forms and formats and for varied purposes. In fact, the content marketing industry is set to be worth $412.88 billion by the end of 2021. However, according to the Content Marketing Institute, creating engaging content is the #1 challenge that marketers face today. We under-stand that producing great content requires great writers who understand the business and can weave their message into reader (and search engine) friendly content. In this project, the team has attempted to bridge the gap between writers and projects by using AI and ML tools. We used NLP techniques to analyze thou-sands of publicly available business articles (corpora) to extract various defining factors for each writing sample. Through this project we aim to automate the highly time-consuming, and often biased task of manually shortlisting the most suitable writer for a given content writing requirement. We believe that a tool like this will have far reaching positive implications for both parties - businesses looking for suitable talent for niche writing jobs as well as experienced writers and Subject Matter Experts (SMEs) wanting to lend their services to content marketing projects. The business gets the content they need, the content writer/ SME gets a chance to leverage his or her talent, while the reader gets authentic content that adds real value.  ( 2 min )

Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a novel form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a computational approach to bridge the gap. This paper proposes a generative design approach based on the pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely GPT-3, is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the correlation between the domains within the generated BID concepts. The approach is then tested via a case study in which the fine-tuned models are applied to generate and evaluate light-weighted flying car concepts inspired by nature. The results show our approach can generate BID concepts with good performance.  ( 2 min )

This paper tackles the problem of decomposing binary data using matrix factorization. We consider the family of mean-parametrized Bernoulli models, a class of generative models that are well suited for modeling binary data and enables interpretability of the factors. We factorize the Bernoulli parameter and consider an additional Beta prior on one of the factors to further improve the model's expressive power. While similar models have been proposed in the literature, they only exploit the Beta prior as a proxy to ensure a valid Bernoulli parameter in a Bayesian setting; in practice it reduces to a uniform or uninformative prior. Besides, estimation in these models has focused on costly Bayesian inference. In this paper, we propose a simple yet very efficient majorization-minimization algorithm for maximum a posteriori estimation. Our approach leverages the Beta prior whose parameters can be tuned to improve performance in matrix completion tasks. Experiments conducted on three public binary datasets show that our approach offers an excellent trade-off between prediction performance, computational complexity, and interpretability.  ( 2 min )

Super-resolution suffers from an innate ill-posed problem that a single low-resolution (LR) image can be from multiple high-resolution (HR) images. Recent studies on the flow-based algorithm solve this ill-posedness by learning the super-resolution space and predicting diverse HR outputs. Unfortunately, the diversity of the super-resolution outputs is still unsatisfactory, and the outputs from the flow-based model usually suffer from undesired artifacts which causes low-quality outputs. In this paper, we propose FS-NCSR which produces diverse and high-quality super-resolution outputs using frequency separation and noise conditioning compared to the existing flow-based approaches. As the sharpness and high-quality detail of the image rely on its high-frequency information, FS-NCSR only estimates the high-frequency information of the high-resolution outputs without redundant low-frequency components. Through this, FS-NCSR significantly improves the diversity score without significant image quality degradation compared to the NCSR, the winner of the previous NTIRE 2021 challenge.  ( 2 min )

We study the theory of neural network (NN) from the lens of classical nonparametric regression problems with a focus on NN's ability to adaptively estimate functions with heterogeneous smoothness -- a property of functions in Besov or Bounded Variation (BV) classes. Existing work on this problem requires tuning the NN architecture based on the function spaces and sample sizes. We consider a "Parallel NN" variant of deep ReLU networks and show that the standard weight decay is equivalent to promoting the $\ell_p$-sparsity ($0<p<1$) of the coefficient vector of an end-to-end learned function bases, i.e., a dictionary. Using this equivalence, we further establish that by tuning only the weight decay, such Parallel NN achieves an estimation error arbitrarily close to the minimax rates for both the Besov and BV classes. Notably, it gets exponentially closer to minimax optimal as the NN gets deeper. Our research sheds new lights on why depth matters and how NNs are more powerful than kernel methods.  ( 2 min )

The COVID-19 pandemic has dramatically changed how healthcare is delivered to patients, how patients interact with healthcare providers, and how healthcare information is disseminated to both healthcare providers and patients. Analytical models that were trained and tested pre-pandemic may no longer be performing up to expectations, providing unreliable and irrelevant learning (ML) models given that ML depends on the basic principle that what happened in the past are likely to repeat in the future. ML faced to two important degradation principles, concept drift, when the underlying properties and characteristics of the variables change and data drift, when the data distributions, probabilities, co-variates, and other variable relationships change, both of which are prime culprits of model failure. Therefore, detecting and diagnosing drift in existing models is something that has become an imperative. And perhaps even more important is a shift in our mindset towards a conscious recognition that drift is inevitable, and model building must incorporate intentional resilience, the ability to offset and recover quickly from failure, and proactive robustness, avoiding failure by developing models that are less vulnerable to drift and disruption.  ( 2 min )

In this paper, we develop a novel approach to posterior contractions rates (PCRs), for both finite-dimensional (parametric) and infinite-dimensional (nonparametric) Bayesian models. Critical to our approach is the combination of an assumption of local Lipschitz-continuity for the posterior distribution with a dynamic formulation of the Wasserstein distance, here referred to as Wasserstein dynamics, which allows to set forth a connection between the problem of establishing PCRs and some classical problems in mathematical analysis, probability theory and mathematical statistics: the Laplace method for approximating integrals, Sanov's large deviation principles in the Wasserstein distance, rates of convergence of the mean Glivenko-Cantelli theorem, and estimates of weighted Poincar\'e-Wirtinger constants. Under dominated Bayesian models, we present two main results: i) a theorem on PCRs for the regular infinite-dimensional exponential family of statistical models; ii) a theorem on PCRs for a general dominated statistical model. Some applications of our results are presented for the regular parametric model, the multinomial model, the finite-dimensional and the infinite-dimensional logistic-Gaussian model and the infinite-dimensional linear regression. In general, our results lead to optimal PCRs in finite dimension, whereas in infinite dimension it is shown how the prior distribution may affect PCRs. With regards to infinite-dimensional Bayesian models for density estimation, our approach to PCRs is the first to consider strong norm distances on parameter spaces of functions, such as Sobolev-like norms, as most of the approaches in the classical (frequentist) and Bayesian literature deal with spaces of density functions endowed with $\mathrm{L}^p$ norms or the Hellinger distance.  ( 2 min )

We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IPS) estimation. However, the performance of existing propensity-based methods can suffer significantly from the propensity estimation bias. In fact, most of the previous IPS-based methods require some amount of missing-completely-at-random (MCAR) data to accurately estimate the propensity. This leads to a critical self-contradiction; IPS is ineffective without MCAR data, even though it originally aims to learn recommenders from only missing-not-at-random feedback. To resolve this propensity contradiction, we derive a propensity-independent generalization error bound and propose a novel algorithm to minimize the theoretical bound via adversarial learning. Our theory and algorithm do not require a propensity estimation procedure, thereby leading to a well-performing rating predictor without the true propensity information. Extensive experiments demonstrate that the proposed approach is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data.  ( 2 min )

We provide a general framework for constructing probability distributions on Riemannian manifolds, taking advantage of area-preserving maps and isometries. Control over distributions' properties, such as parameters, symmetry and modality yield a family of flexible distributions that are straightforward to sample from, suitable for use within Monte Carlo algorithms and latent variable models, such as autoencoders. As an illustration, we empirically validate our approach by utilizing our proposed distributions within a variational autoencoder and a latent space network model. Finally, we take advantage of the generalized description of this framework to posit questions for future work.  ( 2 min )

Despite the success of reinforcement learning (RL) for Markov decision processes (MDPs) with function approximation, most RL algorithms easily fail if the agent only has partial observations of the state. Such a setting is often modeled as a partially observable Markov decision process (POMDP). Existing sample-efficient algorithms for POMDPs are restricted to the tabular setting where the state and observation spaces are finite. In this paper, we make the first attempt at tackling the tension between function approximation and partial observability. In specific, we focus on a class of undercomplete POMDPs with linear function approximations, which allows the state and observation spaces to be infinite. For such POMDPs, we show that the optimal policy and value function can be characterized by a sequence of finite-memory Bellman operators. We propose an RL algorithm that constructs optimistic estimators of these operators via reproducing kernel Hilbert space (RKHS) embedding. Moreover, we theoretically prove that the proposed algorithm finds an $\varepsilon$-optimal policy with $\tilde O (1/\varepsilon^2)$ episodes of exploration. Also, this sample complexity only depends on the intrinsic dimension of the POMDP polynomially and is independent of the size of the state and observation spaces. To our best knowledge, we develop the first provably sample-efficient algorithm for POMDPs with function approximation.

Low-rank matrix estimation under heavy-tailed noise is challenging, both computationally and statistically. Convex approaches have been proven statistically optimal but suffer from high computational costs, especially since robust loss functions are usually non-smooth. More recently, computationally fast non-convex approaches via sub-gradient descent are proposed, which, unfortunately, fail to deliver a statistically consistent estimator even under sub-Gaussian noise. In this paper, we introduce a novel Riemannian sub-gradient (RsGrad) algorithm which is not only computationally efficient with linear convergence but also is statistically optimal, be the noise Gaussian or heavy-tailed. Convergence theory is established for a general framework and specific applications to absolute loss, Huber loss, and quantile loss are investigated. Compared with existing non-convex methods, ours reveals a surprising phenomenon of dual-phase convergence. In phase one, RsGrad behaves as in a typical non-smooth optimization that requires gradually decaying stepsizes. However, phase one only delivers a statistically sub-optimal estimator which is already observed in the existing literature. Interestingly, during phase two, RsGrad converges linearly as if minimizing a smooth and strongly convex objective function and thus a constant stepsize suffices. Underlying the phase-two convergence is the smoothing effect of random noise to the non-smooth robust losses in an area close but not too close to the truth. Lastly, RsGrad is applicable for low-rank tensor estimation under heavy-tailed noise where a statistically optimal rate is attainable with the same phenomenon of dual-phase convergence, and a novel shrinkage-based second-order moment method is guaranteed to deliver a warm initialization. Numerical simulations confirm our theoretical discovery and showcase the superiority of RsGrad over prior methods.

Pretrained language models have achieved state-of-the-art performance when adapted to a downstream NLP task. However, theoretical analysis of these models is scarce and challenging since the pretraining and downstream tasks can be very different. We propose an analysis framework that links the pretraining and downstream tasks with an underlying latent variable generative model of text -- the downstream classifier must recover a function of the posterior distribution over the latent variables. We analyze head tuning (learning a classifier on top of the frozen pretrained model) and prompt tuning in this setting. The generative model in our analysis is either a Hidden Markov Model (HMM) or an HMM augmented with a latent memory component, motivated by long-term dependencies in natural language. We show that 1) under certain non-degeneracy conditions on the HMM, simple classification heads can solve the downstream task, 2) prompt tuning obtains downstream guarantees with weaker non-degeneracy conditions, and 3) our recovery guarantees for the memory-augmented HMM are stronger than for the vanilla HMM because task-relevant information is easier to recover from the long-term memory. Experiments on synthetically generated data from HMMs back our theoretical findings.

In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i.e. Schr\"odinger bridges). We advocate stochastic control as a finite time and low variance alternative to popular steady-state methods such as stochastic gradient Langevin dynamics (SGLD). Furthermore, we discuss and adapt the existing theoretical guarantees of this framework and establish connections to already existing VI routines in SDE-based models.

Message Passing Neural Networks (MPNNs) are a common type of Graph Neural Network (GNN), in which each node's representation is computed recursively by aggregating representations (messages) from its immediate neighbors akin to a star-shaped pattern. MPNNs are appealing for being efficient and scalable, how-ever their expressiveness is upper-bounded by the 1st-order Weisfeiler-Lehman isomorphism test (1-WL). In response, prior works propose highly expressive models at the cost of scalability and sometimes generalization performance. Our work stands between these two regimes: we introduce a general framework to uplift any MPNN to be more expressive, with limited scalability overhead and greatly improved practical performance. We achieve this by extending local aggregation in MPNNs from star patterns to general subgraph patterns (e.g.,k-egonets):in our framework, each node representation is computed as the encoding of a surrounding induced subgraph rather than encoding of immediate neighbors only (i.e. a star). We choose the subgraph encoder to be a GNN (mainly MPNNs, considering scalability) to design a general framework that serves as a wrapper to up-lift any GNN. We call our proposed method GNN-AK(GNN As Kernel), as the framework resembles a convolutional neural network by replacing the kernel with GNNs. Theoretically, we show that our framework is strictly more powerful than 1&2-WL, and is not less powerful than 3-WL. We also design subgraph sampling strategies which greatly reduce memory footprint and improve speed while maintaining performance. Our method sets new state-of-the-art performance by large margins for several well-known graph ML tasks; specifically, 0.08 MAE on ZINC,74.79% and 86.887% accuracy on CIFAR10 and PATTERN respectively.

Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency. This includes reward shaping, behavioral cloning, and reverse curriculum generation.

Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks. However, MTL must deal with challenges such as: overfitting to low resource tasks, catastrophic forgetting, and negative task transfer, or learning interference. Often, in Natural Language Processing (NLP), a separate model per task is needed to obtain the best performance. However, many fine-tuning approaches are both parameter inefficient, i.e., potentially involving one new model per task, and highly susceptible to losing knowledge acquired during pretraining. We propose a novel Transformer architecture consisting of a new conditional attention mechanism as well as a set of task-conditioned modules that facilitate weight sharing. Through this construction (a hypernetwork adapter), we achieve more efficient parameter sharing and mitigate forgetting by keeping half of the weights of a pretrained model fixed. We also use a new multi-task data sampling strategy to mitigate the negative effects of data imbalance across tasks. Using this approach, we are able to surpass single task fine-tuning methods while being parameter and data efficient (using around 66% of the data for weight updates). Compared to other BERT Large methods on GLUE, our 8-task model surpasses other Adapter methods by 2.8% and our 24-task model outperforms by 0.7-1.0% models that use MTL and single task fine-tuning. We show that a larger variant of our single multi-task model approach performs competitively across 26 NLP tasks and yields state-of-the-art results on a number of test and development sets. Our code is publicly available at https://github.com/CAMTL/CA-MTL.

Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks. However, the current theoretical understanding of self-training only applies to linear models. This work provides a unified theoretical analysis of self-training with deep networks for semi-supervised learning, unsupervised domain adaptation, and unsupervised learning. At the core of our analysis is a simple but realistic "expansion" assumption, which states that a low probability subset of the data must expand to a neighborhood with large probability relative to the subset. We also assume that neighborhoods of examples in different classes have minimal overlap. We prove that under these assumptions, the minimizers of population objectives based on self-training and input-consistency regularization will achieve high accuracy with respect to ground-truth labels. By using off-the-shelf generalization bounds, we immediately convert this result to sample complexity guarantees for neural nets that are polynomial in the margin and Lipschitzness. Our results help explain the empirical successes of recently proposed self-training algorithms which use input consistency regularization.

We introduce a scattering covariance matrix which provides non-Gaussian models of time-series having stationary increments. A complex wavelet transform computes signal variations at each scale. Dependencies across scales are captured by the joint covariance across time and scales of complex wavelet coefficients and their modulus. This covariance is nearly diagonalized by a second wavelet transform, which defines the scattering covariance. We show that this set of moments characterizes a wide range of non-Gaussian properties of multi-scale processes. This is analyzed for a variety of processes, including fractional Brownian motions, Poisson, multifractal random walks and Hawkes processes. We prove that self-similar processes have a scattering covariance matrix which is scale invariant. This property can be estimated numerically and defines a class of wide-sense self-similar processes. We build maximum entropy models conditioned by scattering covariance coefficients, and generate new time-series with a microcanonical sampling algorithm. Applications are shown for highly non-Gaussian financial and turbulence time-series.

Two types of states are widely used in quantum mechanics, namely (deterministic-coefficient) pure states and statistical mixtures. A density operator can be associated with each of them. We here address a third type of states, that we previously introduced in a more restricted framework. These states generalize pure ones by replacing each of their deterministic ket coefficients by a random variable. We therefore call them Random-Coefficient Pure States, or RCPS. We analyze their properties and their relationships with both types of usual states. We show that RCPS contain much richer information than the density operator and mean of observables that we associate with them. This occurs because the latter operator only exploits the second-order statistics of the random state coefficients, whereas their higher-order statistics contain additional information. That information can be accessed in practice with the multiple-preparation procedure that we propose for RCPS, by using second-order and higher-order statistics of associated random probabilities of measurement outcomes. Exploiting these higher-order statistics opens the way to a very general approach for performing advanced quantum information processing tasks. We illustrate the relevance of this approach with a generic example, dealing with the estimation of parameters of a quantum process and thus related to quantum process tomography. This parameter estimation is performed in the non-blind (i.e. supervised) or blind (i.e. unsupervised) mode. We show that this problem cannot be solved by using only the density operator \rho of an RCPS and the associated mean value Tr(\rho A) of the operator A that corresponds to the considered physical quantity. We succeed in solving this problem by exploiting a fourth-order statistical parameter of state coefficients, in addition to second-order statistics. Numerical tests validate this result.

Infographics are an aesthetic visual representation of information following specific design principles of human perception. Designing infographics can be a tedious process for non-experts and time-consuming, even for professional designers. With the help of designers, we propose a semi-automated infographic framework for general structured and flow-based infographic design generation. For novice designers, our framework automatically creates and ranks infographic designs for a user-provided text with no requirement for design input. However, expert designers can still provide custom design inputs to customize the infographics. We will also contribute an individual visual group (VG) designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs in this work. Evaluation results confirm that by using our framework, designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining the same quality as hand-designed infographics templates.

Gaussian processes (GPs) are powerful but computationally expensive machine learning models, requiring an estimate of the kernel covariance matrix for every prediction. In large and complex domains, such as graphs, sets, or images, the choice of suitable kernel can also be non-trivial to determine, providing an additional obstacle to the learning task. Over the last decade, these challenges have resulted in significant advances being made in terms of scalability and expressivity, exemplified by, e.g., the use of inducing points and neural network kernel approximations. In this paper, we propose inducing Gaussian process networks (IGN), a simple framework for simultaneously learning the feature space as well as the inducing points. The inducing points, in particular, are learned directly in the feature space, enabling a seamless representation of complex structured domains while also facilitating scalable gradient-based learning methods. We consider both regression and (binary) classification tasks and report on experimental results for real-world data sets showing that IGNs provide significant advances over state-of-the-art methods. We also demonstrate how IGNs can be used to effectively model complex domains using neural network architectures.

We investigate the average value of the $p$th Dirichlet coefficients of elliptic curves for a prime p in a fixed conductor range with given rank. Plotting this average yields a striking oscillating pattern, the details of which vary with the rank. Based on this observation, we perform various data-scientific experiments with the goal of classifying elliptic curves according to their ranks.

This paper tackles the problem of decomposing binary data using matrix factorization. We consider the family of mean-parametrized Bernoulli models, a class of generative models that are well suited for modeling binary data and enables interpretability of the factors. We factorize the Bernoulli parameter and consider an additional Beta prior on one of the factors to further improve the model's expressive power. While similar models have been proposed in the literature, they only exploit the Beta prior as a proxy to ensure a valid Bernoulli parameter in a Bayesian setting; in practice it reduces to a uniform or uninformative prior. Besides, estimation in these models has focused on costly Bayesian inference. In this paper, we propose a simple yet very efficient majorization-minimization algorithm for maximum a posteriori estimation. Our approach leverages the Beta prior whose parameters can be tuned to improve performance in matrix completion tasks. Experiments conducted on three public binary datasets show that our approach offers an excellent trade-off between prediction performance, computational complexity, and interpretability.

Machine learning training methods depend plentifully and intricately on hyperparameters, motivating automated strategies for their optimisation. Many existing algorithms restart training for each new hyperparameter choice, at considerable computational cost. Some hypergradient-based one-pass methods exist, but these either cannot be applied to arbitrary optimiser hyperparameters (such as learning rates and momenta) or take several times longer to train than their base models. We extend these existing methods to develop an approximate hypergradient-based hyperparameter optimiser which is applicable to any continuous hyperparameter appearing in a differentiable model weight update, yet requires only one training episode, with no restarts. We also provide a motivating argument for convergence to the true hypergradient, and perform tractable gradient-based optimisation of independent learning rates for each model parameter. Our method performs competitively from varied random hyperparameter initialisations on several UCI datasets and Fashion-MNIST (using a one-layer MLP), Penn Treebank (using an LSTM) and CIFAR-10 (using a ResNet-18), in time only 2-3x greater than vanilla training.  ( 2 min )

NOTE: This preprint has a flawed theoretical formulation. Please avoid it and refer to the ICLR22 publication https://openreview.net/forum?id=q7n2RngwOM. Also, arXiv:2109.15062 contains some new ideas on unobserved Confounding. As an important problem of causal inference, we discuss the identification and estimation of treatment effects under unobserved confounding. Representing the confounder as a latent variable, we propose Intact-VAE, a new variant of variational autoencoder (VAE), motivated by the prognostic score that is sufficient for identifying treatment effects. We theoretically show that, under certain settings, treatment effects are identified by our model, and further, based on the identifiability of our model (i.e., determinacy of representation), our VAE is a consistent estimator with representation balanced for treatment groups. Experiments on (semi-)synthetic datasets show state-of-the-art performance under diverse settings.  ( 2 min )

The US National Institute of Standards and Technology (NIST) has been conducting a second iteration of the CTS challenge since August 2020. The current iteration of the CTS Challenge is a leaderboard-style speaker recognition evaluation using telephony data extracted from the unexposed portions of the Call My Net 2 (CMN2) and Multi-Language Speech (MLS) corpora collected by the LDC. The CTS Challenge is currently organized in a similar manner to the SRE19 CTS Challenge, offering only an open training condition using two evaluation subsets, namely Progress and Test. Unlike in the SRE19 Challenge, no training or development set was initially released, and NIST has publicly released the leaderboards on both subsets for the CTS Challenge. Which subset (i.e., Progress or Test) a trial belongs to is unknown to challenge participants, and each system submission needs to contain outputs for all of the trials. The CTS Challenge has also served, and will continue to do so, as a prerequisite for entrance to the regular SREs (such as SRE21). Since August 2020, a total of 53 organizations (forming 33 teams) from academia and industry have participated in the CTS Challenge and submitted more than 4400 valid system outputs. This paper presents an overview of the evaluation and several analyses of system performance for some primary conditions in the CTS Challenge. The CTS Challenge results thus far indicate remarkable improvements in performance due to 1) speaker embeddings extracted using large-scale and complex neural network architectures such as ResNets along with angular margin losses for speaker embedding extraction, 2) extensive data augmentation, 3) the use of large amounts of in-house proprietary data from a large number of labeled speakers, 4) long-duration fine-tuning.  ( 2 min )

Estimating the effects of continuous-valued interventions from observational data is critically important in fields such as climate science, healthcare, and economics. Recent work focuses on designing neural-network architectures and regularization functions to allow for scalable estimation of average and individual-level dose response curves from high-dimensional, large-sample data. Such methodologies assume ignorability (all confounding variables are observed) and positivity (all levels of treatment can be observed for every unit described by a given covariate value), which are especially challenged in the continuous treatment regime. Developing scalable sensitivity and uncertainty analyses that allow us to understand the ignorance induced in our estimates when these assumptions are relaxed receives less attention. Here, we develop a continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds that agree with both the observed data and a researcher-defined level of hidden confounding. We introduce a scalable algorithm to derive the bounds and uncertainty-aware deep models to efficiently estimate these bounds for high-dimensional, large-sample observational data. We validate our methods using both synthetic and real-world experiments. For the latter, we work in concert with climate scientists interested in evaluating the climatological impacts of human emissions on cloud properties using satellite observations from the past 15 years: a finite-data problem known to be complicated by the presence of a multitude of unobserved confounders.  ( 2 min )

The great success of deep neural networks is built upon their over-parameterization, which smooths the optimization landscape without degrading the generalization ability. Despite the benefits of over-parameterization, a huge amount of parameters makes deep networks cumbersome in daily life applications. Though techniques such as pruning and distillation are developed, they are expensive in fully training a dense network as backward selection methods, and there is still a void on systematically exploring forward selection methods for learning structural sparsity in deep networks. To fill in this gap, this paper proposes a new approach based on differential inclusions of inverse scale spaces, which generate a family of models from simple to complex ones along the dynamics via coupling a pair of parameters, such that over-parameterized deep models and their structural sparsity can be explored simultaneously. This kind of differential inclusion scheme has a simple discretization, dubbed Deep structure splitting Linearized Bregman Iteration (DessiLBI), whose global convergence in learning deep networks could be established under the Kurdyka-Lojasiewicz framework. Experimental evidence shows that our method achieves comparable and even better performance than the competitive optimizers in exploring the sparse structure of several widely used backbones on the benchmark datasets. Remarkably, with early stopping, our method unveils `winning tickets' in early epochs: the effective sparse network structures with comparable test accuracy to fully trained over-parameterized models, that are further transferable to similar alternative tasks. Furthermore, our method is able to grow networks efficiently with adaptive filter configurations, demonstrating a good performance with much less computational cost. Codes and models can be downloaded at {https://github.com/DessiLBI2020/DessiLBI}.  ( 3 min )

Generalization bounds are a critical tool to assess the training data requirements of Quantum Machine Learning (QML). Recent work has established guarantees for in-distribution generalization of quantum neural networks (QNNs), where training and testing data are assumed to be drawn from the same data distribution. However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a distribution different from the training distribution. In this work, we prove out-of-distribution generalization for the task of learning an unknown unitary using a QNN and for a broad class of training and testing distributions. In particular, we show that one can learn the action of a unitary on entangled states using only product state training data. We numerically illustrate this by showing that the evolution of a Heisenberg spin chain can be learned using only product training states. Since product states can be prepared using only single-qubit gates, this advances the prospects of learning quantum dynamics using near term quantum computers and quantum experiments, and further opens up new methods for both the classical and quantum compilation of quantum circuits.  ( 2 min )

We present a general framework for training safe agents whose naive incentives are unsafe. As an example, manipulative or deceptive behaviour can improve rewards but should be avoided. Most approaches fail here: agents maximize expected return by any means necessary. We formally describe settings with 'delicate' parts of the state which should not be used as a means to an end. We then train agents to maximize the causal effect of actions on the expected return which is not mediated by the delicate parts of state, using Causal Influence Diagram analysis. The resulting agents have no incentive to control the delicate state. We further show how our framework unifies and generalizes existing proposals.  ( 2 min )

Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter. The development of marginal feature importance methods, such as marginal contribution feature importance, attempts to break this trend by providing a useful framework for explaining relationships in data in an interpretable fashion. In this work, we generalize the framework of marginal contribution feature importance to improve performance with regards to detecting correlated interactions and reducing runtime. To do so, we consider "information subsets" of the set of features $F$ and show that our importance metric can be computed directly after applying fair representation learning methods from the AI fairness literature. The methods of optimal transport and linear regression are considered and explored experimentally for removing all the information of our feature of interest $f$ from the feature set $F$. Given these implementations, we show on real and simulated data that ultra marginal feature importance performs at least as well as marginal contribution feature importance, with substantially faster computation time and better performance in the presence of correlated interactions and unrelated features.  ( 2 min )

I want to create a NN that takes these readings and makes predictions about "what will the readings be in 5 minutes if I turn the AC on?". I'm thinking of training it with "the angle of the sun at that time", "temp", "humidity", and "ac/heater/fan state"(3) and then extracting data pairs spaced by 5 minutes where the system was in that state for the entire interval. Then I'm thinking I should use the 5-minute-later apparent temp as the training output. So the NN ultimately answers the question "what would be the apparent temperature if the system were to be in the given state for the next 5 minutes?" Am I on the right track here? submitted by /u/HasFiveVowels [link] [comments]  ( 1 min )

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Like I mark some areas of my pictures and then selcect what should happend with them? submitted by /u/xXLisa28Xx [link] [comments]

Is artbreeder the best way to do it, or is there a better way? submitted by /u/xXNOdrugsForMEXx [link] [comments]  ( 1 min )

Programming is way more fun when you learn/work with someone. Help each other, ask questions, brainstorm, etc. There is just so much benefit to joining a community when you are in this field, especially when you cannot find the question you are looking for on stack overflow! 😉 This is the same thing with AI, and it is why a little less than two years ago I created a discord server. Where anyone learning or working in the field could come and share their projects, learn together, work together, and much more. The community has now over 20 000 members, which is unbelievable! So glad to see it growing and see everyone so active. We also have an amazing partnership with an AI company coming that is super exciting for the community. You definitely want to be there to enjoy all the benefits they will give us. Come join us if you are in the field of AI ! https://discord.gg/learnaitogether submitted by /u/OnlyProggingForFun [link] [comments]  ( 1 min )

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https://arxiv.org/abs/2204.09123 https://www.researchgate.net/publication/360079336_GAMe_changer_or_not_An_evaluation_of_interpretable_machine_learning_models_based_on_additive_model_constraints submitted by /u/Positive_Ad_1090 [link] [comments]

Kornia is a differentiable library for computer vision based on PyTorch. Does anyone have experience with their SIFT descriptor. What can you differentiate? submitted by /u/avd4292 [link] [comments]

When comes to evaluating and selecting a model, should one focus on minimizing loss (i.e., sparse categorical crossentropy) or obtain high rated metrics (i.e., f1)? Often time the model highest rated metrics would generate higher loss than ones with lower ratings in metrics during validation/test sets. Some would say focus on metrics as loss are for the machine to optimize learning, what stays in the training, stays in the training. However, wouldn't loss be also an important element to consider since it also describe the performance of the model, particularly when obtained from the test set? How should one prioritize? Metrics/loss rules all or seek for balance? submitted by /u/Hydraze [link] [comments]  ( 1 min )

Assume to have a 2-step algorithm: 1) aggregate data points into clusters 2) feed the clusters to a downstream task (e.g. classification, regression, etc). Is there any work that explores how to optimize the clustering in 1) to achieve the best performance in the downstream task 2)? One example would be a differentiable clustering algorithm that receives gradients from the downstream task or a parametrized clustering algorithm whose parameters are automatically tuned to increase the performance of the downstream task. I have found very little on this topic in the literature, could you point me to some relevant work? submitted by /u/fedetask [link] [comments]  ( 1 min )

I am classifying social media posts (facebook, instagram), with emojis being upwards of 100% of content. For example, you may want to tag "🤮🤮🤮" as in need for moderation, and "🤔🤔🤔" as prioritized for a response. Looking for a good model to fine tune I found BerTweet, which seems at least somewhat emoji aware. However it also has a ton of out-of-vocabulary results, both for emoji and semi-common English words, despite it's liberal use of emoji.demojize and splitting up more complex emoji: ​ https://preview.redd.it/t6ai3o8le1v81.png?width=687&format=png&auto=webp&s=c16157addbe1b3d34858708f3e6c7517e64d26ec A model like `xlm-roberta-base with a larger vocabulary (250k) and more robust tokenization seems to have some 500 emoji directly in its vocabulary directly, without converting them to text. This seems potentially more promising, but also guarantees a token like 🤮 is just out of vocabulary rather than being interpreted by word pieces. Has anyone here had experience with dealing with emoji in text classification, and what approaches were most successful? submitted by /u/sanderbaduk [link] [comments]  ( 2 min )

Hi, I have a question about how to use metric network after contrastive learning. If I have trained a network well with NCELoss, I would like to finetune this network to match the best output by input(It used at calculating NCELoss). Is there any good way to do it? ​ Thank you for reading! submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 1 min )

We’d like to know if anyone is interested in participating in a mock peer review? Basically if you have a paper you’d like to get feedback on, and would like to review others’ papers in exchange, you’re welcome to continue reading. We are gauging public interest in mock peer review and exploring the possibility to host the reviews on DouBlind. We’d like to know your answers to the following questions: Are you interested in mock peer review? Do you want to do this privately (paper and review are kept inside a small group) or openly (paper and review are open)? How many papers do you like to review? Do you have any concerns? submitted by /u/DouBlindDotCOM [link] [comments]  ( 2 min )

This is reproduced from Zhihu and translated by DeepL, only used for enthusiasts to communicate. ​ MindSpore, as an end-to-edge cloud collaborative full-scenario AI open source framework, takes into account the flexibility of academic research and the high-performance needs of industry, supports end-to-edge cloud full-scene business, and brings developers a simpler programming, easier debugging, superior performance, and more flexible deployment experience, which has received widespread attention and application in the industry and has been open source on 2020.3.28, and is the Gitee The highest index of open source software. Welcome to participate in open source contributions, model crowdsourcing collaboration, industry innovation and application, algorithm innovation, academic collabora…  ( 3 min )

Hi everyone, this is my first post please go easy on me. I'm currently playing around with a bigger Model in tf-agents. I worked only with structured data (TF, SKlearn, Pandas...). Now I'm struggling a bit with the preprocessing and where in the architecture to place it. I use multiple Inputs and encoding layers for each of them. For the training of the Encoders I used some SKLearn pre-processor (StandardScaler, MinMaxScaler, KBinsDiscretizer). I try to reuse the pre-processing pipeline in the model or extract the information for other pre-processing mechanisms(e.g. pre-processing tf layers) My current options I came up with: Incorporate it directly into the Environment and return the pre-processed observation Pro easy, can probably use my SKlearn pipeline Contra I'd like to keep the architecture clean, so the environment should only give out raw values and not prepared values for a certain model Use an environment wrapper around the "raw" environment Pro "raw" environment needs no tuning Contra not sure if I can use my pipeline here and not sure if I'm taking a bad path here Use pre-processing TF layers Pro most of the API is there and can be used in my Encodernetworks, seems to be the TFic way Contra the SKlearn pre-processor have values for each column. The layers (e.g. rescale ) seems to only take one configuration for a whole tensor. I could probably create a layer for each value in the Tensor but that doesn't feel that it is supposed to be like that. If you have more options or can share your experience one of the options mentioned above I would be very glad. submitted by /u/Kjiessar [link] [comments]  ( 1 min )

I am using PPO in the context of multi-agent RL. I was wondering if PyTorch has a way of handling when hidden states should be reinitialized to zeros. What I have found is this implementation: def forward(self, x, hxs, masks): if x.size(0) == hxs.size(0): x, hxs = self.rnn(x.unsqueeze(0), (hxs * masks.repeat(1, self._recurrent_N).unsqueeze(-1)).transpose(0, 1).contiguous()) x = x.squeeze(0) hxs = hxs.transpose(0, 1) else: # x is a (T, N, -1) tensor that has been flatten to (T * N, -1) N = hxs.size(0) T = int(x.size(0) / N) # unflatten x = x.view(T, N, x.size(1)) # Same deal with masks masks = masks.view(T, N) # Let's figure out which steps in the sequence have a zero for any agent # We will always assume t=0 has a zero in it as that makes the logic cleaner has_zeros = ((masks[1:] == 0.0) .any(dim=-1) .nonzero() .squeeze() .cpu()) # +1 to correct the masks[1:] if has_zeros.dim() == 0: # Deal with scalar has_zeros = [has_zeros.item() + 1] else: has_zeros = (has_zeros + 1).numpy().tolist() # add t=0 and t=T to the list has_zeros = [0] + has_zeros + [T] hxs = hxs.transpose(0, 1) outputs = [] for i in range(len(has_zeros) - 1): # We can now process steps that don't have any zeros in masks together! # This is much faster start_idx = has_zeros[i] end_idx = has_zeros[i + 1] temp = (hxs * masks[start_idx].view(1, -1, 1).repeat(self._recurrent_N, 1, 1)).contiguous() rnn_scores, hxs = self.rnn(x[start_idx:end_idx], temp) outputs.append(rnn_scores) # assert len(outputs) == T # x is a (T, N, -1) tensor x = torch.cat(outputs, dim=0) # flatten x = x.reshape(T * N, -1) hxs = hxs.transpose(0, 1) submitted by /u/No_Possibility_7588 [link] [comments]  ( 2 min )

I don't think an opensource one exists but figured I'd ask here because you never know what's laying around the internet! As an aside, if one doesn't exist, let me know if you're interested in partnering in writing one! Edit: For anyone wondering I need the env to be in jax because my muzero implementation is in jax and I need the env to run on TPU cores, not CPU submitted by /u/evanatyourservice [link] [comments]  ( 1 min )

I am looking for any papers that do the following: use neural networks in the RL pipeline as the state space is too large for calculating the optimal policy using the traditional tabular value iteration or policy iteration. In this setting, the model is completely known, i.e., no learning. Most papers I see with DeepRL assume that the transition probabilities are unknown and that they have access to a simulator that gives them the ability to query data points. I am looking for existing work in DeepRL where the transition probabilities are known but the problem is intractable using tabular methods. Any direction would be appreciated, thanks! submitted by /u/lolillini [link] [comments]  ( 1 min )

Hello, im making a new post as i couldnt find any answers to this before (although this reddit post is similar to my issue) I am trying to implement a simple multivariate Gaussian policy without neural networks, basically using a standard policy gradient update with SGD + score function gradient, without batches. The reason for this is to avoid unstable updates, meaning too large updates in mean/variance. The idea is thus to use a trust region update, to keep the updates within some reasonable size. I am a little confused regarding the maximization of the surrogate objective. As seen in this stackoverflow post, we wish to maximize [pi/pi_old] , compared to [log(pi)] in vanilla PG. Since i do not use automatic differentiation, but one single stochastic descent, how do I find the gradient of pi/pi_old ? To my understanding, the flow of the algorithm is this: sample experience -> compute new policy parameters -> compare with previous policy -> construct surrogate function -> perform SGD on surrogate to get the actual new policy It is the last step i am struggling with. submitted by /u/Acrobatic-Ad-9189 [link] [comments]  ( 1 min )

I'll be starting my work for object manipulation using deep RL, and i would like to get start from the scratch, please recommend the source, tools, and software used for this purpose. but not be working on modeling the robot, instead will be using any robot with gripper which can be interfaced with ROS. Also please link the github repositories which can be helpfull in the learning process Thanks submitted by /u/Western-Age3148 [link] [comments]  ( 1 min )

Hi, I want to check policy-encoding mapping e : (S → A) → R^k in Universal Successor Features Approximators. I don't know how to embedding network to another network. There are too many weights! Do you have any ideas? Thank you for reading! submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 1 min )

Found a useful list of Tools, Frameworks, and Resources for RL/ML. It covers Reinforcement learning, Machine Learning (TensorFlow & PyTorch), Core ML, Deep Learning, Computer Vision (CV). I thought I'd share it for anyone that's interested submitted by /u/Khaotic_Kernel [link] [comments]  ( 1 min )

Posted by Ting Chen and David Fleet, Research Scientists, Google Research, Brain Team Object detection is a long-standing computer vision task that attempts to recognize and localize all objects of interest in an image. The complexity arises when trying to identify or localize all object instances while also avoiding duplication. Existing approaches, like Faster R-CNN and DETR, are carefully designed and highly customized in the choice of architecture and loss function. This specialization of existing systems has created two major barriers: (1) it adds complexity in tuning and training the different parts of the system (e.g., region proposal network, graph matching with GIOU loss, etc.), and (2), it can reduce the ability of a model to generalize, necessitating a redesign of the model for…  ( 7 min )

AWS CodeArtifact allows developers to connect internal code repositories to upstream code repositories like Pypi, Maven, or NPM. AWS CodeArtifact is a powerful addition to CI/CD workflows on AWS, but it is similarly effective for code-bases hosted on a Jupyter notebook. This is a common development paradigm for Machine Learning developers that build and train […]  ( 9 min )

Artificial Intelligence is all over the world today. From the use of virtual assistants like Siri, Alexa, or Cortana, to improving…  ( 2 min )

Different parts of the globe are experiencing distinct climate challenges — severe drought, dangerous flooding, reduced biodiversity or dense air pollution. The challenges are so great that no country can solve them on their own. But innovative startups worldwide are lighting the way, demonstrating how these daunting challenges can be better understood and addressed with Read article > The post By Land, Sea and Space: How 5 Startups Are Using AI to Help Save the Planet appeared first on NVIDIA Blog.  ( 3 min )

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I'm a software developer but I'm not really experienced in AI. Would it be best to train first for speech bubbles and separately for panel drawings? What kind of network is the best for this? Just thinking that it would be a cool project to have auto generated legible infinite comic strips for a semi niche comic strip that runs in my country. submitted by /u/dananite [link] [comments]  ( 1 min )

Content generation is such a time-suck for small businesses, and it seems like an interesting vertical to apply AI. The AI would generate the content after being given a prompt. There are already a few tools trying this, but the quality doesn't seem to be very high. Are there better tools that I'm missing, or is the consumer-facing software so early-stage that it would be better to hire a data scientist and train an AI system specifically for this purpose? https://www.reddit.com/r/MachinesWrite/comments/f45eav/list_of_ai_text_generators/?utm_source=share&utm_medium=web2x&context=3 https://www.reddit.com/r/juststart/comments/axa8w3/ai_ml_text_generators/?utm_source=share&utm_medium=web2x&context=3 submitted by /u/CliffWoolum [link] [comments]  ( 1 min )

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Here's a tutorial and lightweight PyTorch implementation of VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. Hope you find it helpful! submitted by /u/thejashGI [link] [comments]

Deep neural network (DNN) models for object recognition and classification, such as Yolo, ResNet, and EfficientNet, are used in video analytics applications such as urban mobility and smart automobiles. There is a symbiotic link between edge computing and video analytics, claiming that live video analytics is the “killer app” for edge computing. Edge devices come in various sizes and designs, but they are always resource-constrained compared to the cloud. Video analytics deployments send the videos to on-premises edge servers. The article handles the difficulty of supporting inference and retraining jobs on edge servers simultaneously, which necessitates navigating the fundamental tradeoff between the accuracy of the retrained model and the accuracy of the inference. Edge computation is preferred for video analytics because it eliminates the need for expensive network lines to broadcast videos to the cloud while simultaneously preserving video privacy. Edge computation has a finite amount of resources (e.g., with weak GPUs). The mismatch between the increasing rate of model compute needs, and the total cycles of processors exacerbate this problem. As a result, model compression is used in edge deployments. Continue reading our bite on this research Paper: https://www.microsoft.com/en-us/research/uploads/prod/2021/07/nsdi22spring-final74.pdf Github: https://github.com/edge-video-services/ekya submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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CypherZilla on OpenSea https://reddit.com/link/u8he59/video/i9a29i5ywtu81/player submitted by /u/thecypherbeast [link] [comments]

https://www.sganalytics.com/blog/top-ethical-challenges-in-ai-the-price-of-progress/ submitted by /u/JencyJane [link] [comments]

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Hello I have a question. For example, in the case of an inverted pendulum or cartpole, I train the model for the pole to be at 0 degrees (vertical) and it works. Then I want this same model to keep the pole at another position, for example, 3 degrees, do I have to train this model again for achieving this to or can I somehow use the model I already trained and what it learnt and input the new position I want it to be? idk if I explained myself I guess its mostly doubts about how to interact with the model and how to properly use a model that has already been trained. If anyone has some example of code (python, gym), on interacting with a trained model it would be really helpful. submitted by /u/Sleyck [link] [comments]  ( 1 min )

https://github.com/marlbenchmark/on-policy/blob/main/onpolicy/algorithms/r_mappo/r_mappo.py submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

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I'm working on a project where I'm trying to prove the existence of a particular set of functions by showing it can be constructed as the solution to a Markov Decision Process. However, it seems that it's much simpler to convert it to a partially observable MDP, rather than a classic one. I know it's been proven that the set of optimal policies for a classic MDP is nonempty, and intuitively I feel like the same should hold for POMDPs, but I'm having a hard time finding a particular source proving such a thing. Does anyone know where I ought to look? submitted by /u/LessPoliticalAccount [link] [comments]  ( 1 min )

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In the first edition of the textbook, the section on actor-critic methods (link) describes the classical approach of using the temporal difference error 𝛿 to modify the probability of selecting action a in state s: https://preview.redd.it/fa35vut7ewu81.png?width=238&format=png&auto=webp&s=c1b8952b065a90ecd2b8c0c30b985e36d37dbc30 Then they briefly mention that one variation on the classical approach is to scale temporal difference error 𝛿 by the inverse of the probability of selecting the action a, where that probability is given by 𝜋(s, a): https://preview.redd.it/69gd3zmbdwu81.png?width=375&format=png&auto=webp&s=b66a7d5eef3c2b7bc256473aed728223921a751c They say: " These issues were explored early on, primarily for the immediate reward case (Sutton, 1984; Williams, 1992) and have not been brought fully up to date." This idea is relevant to a project I'm working on, and I'd like to read more about it. But the references seem to be dead ends: Sutton 1984 is his PhD thesis, which I can't find a digital copy of, and Williams 1992 is this paper, which doesn't seem to contain this idea. Also this section doesn't seem to appear in the second edition of the textbook. You folks are much smarter than I am: Does modifying the update in this way mean anything to you? Are there modern approaches that do something like this? Or should I assume it was a little-explored idea in the early days that has been more-or-less forgotten? Thanks very much! submitted by /u/Careless-Argument-37 [link] [comments]  ( 2 min )

I was wondering what methods there are for RL with time delay other than augmenting the state space with the action buffer or using a model to undelay the environment. I've seen this post How to deal with the time delay in reinforcement learning? - Artificial Intelligence Stack Exchange however it's rather brief and I wondered if there were any more recent advancements. I am also struggling to understand partial trajectory resampling ( 2010.02966.pdf (arxiv.org) ) and the code in the accompanying repo. GitHub - rmst/rlrd: PyTorch implementation of our paper Reinforcement Learning with Random Delays (ICLR 2020) I was wondering how we can resample actions in environments with constant delays if those actions are used in the state space for all subsequent chosen actions? submitted by /u/SuperDuperDooken [link] [comments]  ( 1 min )

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Hi, I want to train my agent in the environment used in "Emergence of Locomotion Behaviours in Rich Environments". Here is a video about that https://www.youtube.com/watch?v=hx_bgoTF7bs. Is the environment released? Thanks for reading. submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 1 min )

Hi everyone. Today we released the mGPT model: multilingual generative pre-trained transformer The checkpoints are available on Huggingface model page The example usage is at the Github repo https://github.com/ai-forever/mgpt The model has 1.3 billion parameters The context length is 512 tokens. The model can generate sequences after the input prompt, can be used for fine-tuning or for zero- and few-shot learning: from transformers import GPT2LMHeadModel, GPT2Tokenizer model_name = "sberbank-ai/mGPT" tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name) model.cuda() model.eval() texts = [ "My favourite holiday is ", "Իմ սիրելի տոնն է ", "Моє улюблене свято ", "mi fiesta favorita es ", "मेरी पसंदीदा छुट्टी है", "我最喜欢的节日是", "Min…  ( 2 min )

Hi r/MachineLearning! I want to share with you a fun side project of mine on benchmarking the GPUs for deep learning: [project page]. https://preview.redd.it/7olwqyze5yu81.png?width=2041&format=png&auto=webp&s=25aecb9733366720a2be5cecc2048eb2a734c9b9 Here are some key features: It helps to estimate the runtime of algorithms on a different GPU. It measures GPU processing speed independent of GPU memory capacity. It contains adjustable weightings through interactive UIs. The project page also explains how this benchmark differs from existing ones, and why this benchmark is more relevant to academic research. I would love to know what you think! submitted by /u/roll-a-dice [link] [comments]  ( 1 min )

I'm using mbp 2015, it's a pretty solid laptop, I like it a lot, though it feels slow and I've started to look for a replacement. Given that I run all experiment on gpu dedicated servers, my laptop serves me as a typewriter, it's ok, but I'd like to get more out of it. Frankly I'm a bit disappointed by 2021 Macbooks, hope they'll be improved in 2022. Recently lambda labs together with razer announced their tensorbook https://lambdalabs.com/deep-learning/laptops/tensorbook , their pricing looks weird to me, the more you pay the more years of support you have, that's the only thing which differentiates base bundle from enterprise. Also there is no option to customize hardware for it, though basic bundle itself looks ok, its price is $3500 like M1 Max's. What's your opinion about this laptop in particular? would you buy it? generally this laptop looks like a cool thing to have for local model development even from a tent somewhere in Nepal, given that you have enough power banks to charge it. :) What's your choice of a laptop for DL? My biggest requirement is a durable laptop which will serve at least 5 years, better with NVIDA GPU for development and debugging. submitted by /u/taras-sereda [link] [comments]  ( 1 min )

​ Transformations that alter the perception of target faces Paper: https://www.pnas.org/doi/10.1073/pnas.2115228119 Dataset: https://onemillionimpressions.com/ submitted by /u/joshuacpeterson [link] [comments]

submitted by /u/Wiskkey [link] [comments]

Here's a tutorial and lightweight PyTorch implementation of VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. Hope you find it helpful! submitted by /u/thejashGI [link] [comments]

Hi folks. This morning I released the new cleanlab 2.0 Python package for automatically finding errors in datasets and machine learning/analytics with real-world, messy data and labels. tl;dr - cleanlab provides a framework to streamline data-centric AI. https://preview.redd.it/hq1kyasvwwu81.png?width=2279&format=png&auto=webp&s=4fa3c82ec66d685c8fc4f95c5d9a0fc4be192d6b After 1.0 launch last year, engineers used cleanlab at Google to clean and train robust models on speech data), at Amazon to estimate how often the Alexa device doesn’t wake, at Wells Fargo to train reliable financial prediction models, and at Microsoft, Tesla, Facebook, etc. Joined by two good friends from grad school, we completely rebuilt cleanlab 2.0 to work for all data scientists, ML datasets, and models; and hit a…  ( 2 min )

If you are passionate about Data & AI we have the perfect challenge for you! The applications for Galp’s Hackathon Retail 4.0 are OPEN! With this Hackathon, Galp is challenging the community to propose solutions to specific problems and use cases that they think could improve their typical customer journey in the service stations. Gather a team and come up with an innovative solution for a chance of winning 10.000€! Let’s shape the future of Galp's retail? Apply now: https://taikai.network/en/galp/hackathons/retail40 https://preview.redd.it/wkfb6ybuwwu81.png?width=3334&format=png&auto=webp&s=deef13767df5ba607e387ce4e278ae3981d93582 submitted by /u/migueldsalmeida [link] [comments]  ( 1 min )

I'm working on a Machine Learning problem for multi class classification with imbalanced classes distribution, so obviously my model favours classes with more data and fails to predict classes with few data, what are the techniques I can use to help the model distinguish all the classes the same way ? P.S I'm avoiding to use SMOTE method to train the model on real used data rather than generated submitted by /u/According-Promise-23 [link] [comments]  ( 2 min )

submitted by /u/SleekEagle [link] [comments]

Dear ML researchers, For the past many years, I've been updating my machine learning research notes for my PhD students and everyone online continuously. I don't like uploading to arxiv to get "citations", and GitHub serves me well: Hope they are useful for you: https://github.com/roboticcam/machine-learning-notes Richard, submitted by /u/MLknowledge [link] [comments]  ( 1 min )

Hi, I am performing a Image --> scalar regression. The output scalar I am trying to estimate follows a roughly Gaussian distribution. I notice that the DNN output is biased to output values towards the mean (makes sense). ​ This seems like a problem of imbalanced data. For classification, I can oversample minority classes. What is the equivalent for regression? Is there an equivalent technique for regression where we oversample "outliers" and undersample central values. submitted by /u/rsandler [link] [comments]  ( 1 min )

Hi, I made a video explaining the ideas behind building a Dense Passage Retriever(DPR). Whenever we talk about retrievers, we mostly refer to the DPR formulation which appeared in this paper. A lot of publicly available implementations also use this formulation. In a previous video, we discussed how to use the DPR End-to-End QA system which uses DPR with a QA model. In this video, we solely focus on retrievers and the ideas behind building them. The implementation is quite similar to retrievers pre-trained with Inverse Close Task. This video is part 8 of 9 video series on Open-domain question answering using Dense retrievers. Thanks for the support and I will appreciate any feedback. https://www.youtube.com/watch?v=w61p0HLo7gc submitted by /u/infiniteakashe [link] [comments]  ( 1 min )

Hi, I have been studying super-resolution with gans and took a look at SRGAN et ESRGAN. I have spent the whole day running experiments in order to find if I can manage to overfit on a single batch of 16 / 32 / 128 examples (MNIST). I have found out that it's almost impossible to use this tactic as a sanity check because it simply cannot generate good quality samples. I would like to know what are your thoughts on this, and how you would run sanity checks regarding GANs. ​ Thank you ! submitted by /u/Frizzoux [link] [comments]  ( 1 min )

https://www.amazon.science/blog/amazon-releases-51-language-dataset-for-language-understanding submitted by /u/__lawless [link] [comments]

Hi all, I have csv-files (~300) with a fixed set of columns (~40) but varying number of rows (sum of all rows ~300 000) and multiple labels per csv that I want to predict. Because of the limited number of csv-files and as a first try I am predicting the labels row-wise (attaching the label to all the rows of one csv-file) which works well for some labels but not for others. Currently, I am calculating some features for every row and just appending them to the row and some features for the whole csv-file and appending them to every row. Two problems are now arising that I would like to hear some input about: The number of features per csv is growing and it seems like a waste to copy them to every row. For some labels it is probably reasonable to throw away most of the rows and only feed in a handful. How would you design a structure that incorporates the limited number of csv-files and the different ways to treat features (row vs. csv)? submitted by /u/tlklk [link] [comments]  ( 2 min )

Hi! I am an undergrad and I am going to start my MS in CS this fall. My research interest is mainly in Multimodal Learning for language and Speech. I have written papers before but both my papers have been peer reviewed journal papers (Knowledge-Based Systems, Elsevier) [1] [2] I now want to start publishing papers in conferences since I have noticed that it is much easier to get noticed and recieve reviews when the paper is presented at a conference. I want to understand how different is the publication process for conferences? I also wanted recommendations on conferences in the NLP and Speech area, considering this will be my first conference paper. Thanks! (I would also appreciate reviews on my papers if anyone has the time to look them over. Thanks!) submitted by /u/prabhav55221 [link] [comments]  ( 4 min )

Basically, I don't want to phrase this as a a "how-to" post but arxiv-sanity-lite really bothers me. How do you guys find recent papers in your area of interest which are promising besides following what is published at major conferences? I believe the website is "too lightweight". For example, what if I am interested in computer vision papers and I specify that in the tags field (i.e. explicitly typing "computer vision"). How can I list the papers based on a score (basically goodness of the paper)? Why does using shortcuts (basically links) like `````recommend over last week or recommend over last 3 days always (at least for me) end up with 0 results? I've never used the original arxiv-sanity before so I strongly believe that there is something that I am missing. submitted by /u/Icy_Fisherman7187 [link] [comments]  ( 1 min )

The Norwegian University of Science and Technology (NTNU) has a vacancy for PhD Candidate within the DIGITALSEAICE project . The project aims to build a multi-scale digital infrastructure that integrates local and regional sea ice models for improved forecasting and understanding of variations in polar ice conditions. More information here: https://www.jobbnorge.no/en/available-jobs/job/224802/ submitted by /u/KatjaKim [link] [comments]  ( 1 min )

We are working on a book that focuses on deep learning efficiency techniques such as quantization, pruning, distillation, etc. for both server-side as well as on-device (smartphones, IoT, etc.) applications. The goal is to introduce these ideas in a single place, without having to parse many papers, try to get a working code sample, and then spend time debugging. With the accompanying codelabs, we hope that our readers can make their models 4-20x smaller, faster, and better in quality. We have released the first four chapter's draft PDFs, and would truly appreciate any sort of comments / feedback. Book: efficientdlbook.com Feedback: hello@efficientdlbook.com submitted by /u/EfficientDLBook [link] [comments]  ( 1 min )

https://youtu.be/ccBMRryxGog This video is an interview with Barret Zoph and William Fedus of Google Brain about Sparse Expert Models. Sparse Expert models have been hugely successful at distributing parts of models, mostly Transformers, across large array of machines and use a routing function to effectively route signals between them. This means that even though these models have a huge number of parameters, the computational load for a given signal does not increase because the model is only sparsely activated. Sparse expert models, such as Switch Transformers and GLAM can scale up to trillions of parameters and bring a number of desirable properties. We discuss everything from the fundamentals, history, strengths and weaknesses, up to the current state of the art of these models. ​ OUTLINE: 0:00 - Intro 0:30 - What are sparse expert models? 4:25 - Start of Interview 5:55 - What do you mean by sparse experts? 8:10 - How does routing work in these models? 12:10 - What is the history of sparse experts? 14:45 - What does an individual expert learn? 19:25 - When are these models appropriate? 22:30 - How comparable are sparse to dense models? 26:30 - How does the pathways system connect to this? 28:45 - What improvements did GLAM make? 31:30 - The "designing sparse experts" paper 37:45 - Can experts be frozen during training? 41:20 - Can the routing function be improved? 47:15 - Can experts be distributed beyond data centers? 50:20 - Are there sparse experts for other domains than NLP? 52:15 - Are sparse and dense models in competition? 53:35 - Where do we go from here? 56:30 - How can people get started with this? ​ Papers: Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (https://arxiv.org/abs/2101.03961) GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (https://arxiv.org/abs/2112.06905) Designing Effective Sparse Expert Models (https://arxiv.org/abs/2202.08906) submitted by /u/ykilcher [link] [comments]  ( 1 min )

Next week ICLR 2022 is taking place. Fully virtual and 1000+ high quality papers. To make sense of this volume of papers we have indexed the papers and provide an interactive semantic map of #ICLR2022, check out: https://search.zeta-alpha.com/?q=&d=ly&doc_sources=ICLR&sort_by=authority To enjoy the full map, click on [Explore more] and then enter full screen mode. We will also discuss the program and 10 must read papers in the Zeta Alpha "Trends in AI" ICLR edition webinar Monday 25th, for which you can sign up here. https://us06web.zoom.us/webinar/register/7816505274568/WN_82DzwhXZQbOCSTWgaI9xMw Looking forward to meet you online at ICLR 2022! https://preview.redd.it/6wdqj4ru7uu81.jpg?width=2202&format=pjpg&auto=webp&s=c97417c9ea39919041949bf3aa38ad33bb6eca5a submitted by /u/EngineerZetaAlpha [link] [comments]  ( 1 min )

This article is reproduced from Zhihu and translated by DeepL for enthusiasts to communicate. 1. Research Background High dynamic range images (HDR) are mainly oriented to picture display technology. In a certain scene, if the range of high and low luminance areas exceeds the maximum luminance range of the image, the display effect will be greatly reduced, and HDR is to better solve this problem, it can record a broader range of luminance images, so as to obtain a more effective display effect. The current solution to the problem of generating high dynamic range images (HDR) focuses on the fusion of two low dynamic range (LDR) images of different exposures taken with the same camera. In such a solution by the camera shake or object movement during the exposure time to produce the proble…  ( 3 min )

I am having trouble finding some general information on this subject. I know I am down the rabbit hole when google doesn't have an answer. I want to know best practices and information on using clusters for machine learning with large amounts of data. I believe I have a close to an optimal solution but wanted to get some other opinions on the subject. My current setup: AWS EKS Kubernetes for a cluster Kubeflow for ML platform Katib for HPT jobs Pytorch for custom models Spot instance GPUs Lustre for file serving to the models My Data: Millions of Images stored in S3 ~50TB of data What is the most efficient way to move my data to the cluster? My current approach: Preprocess the data with a dedicated instance and store it in S3 Master runs on a dedicated node Katib spins up a set number of GPU spot nodes A claim is made, and an FSx Lustre system is generated for the pod Advantages: Very fast training and data movement with spot training Disadvantages: I have to spin up several Lustre systems for the training Preprocess the data with a dedicated instance and store it in S3 Possible alternative Same as above but use EFS as a distributed file system so I don't have to wait for Lustre Advantages: Potentially cheaper as I have only one FS Disadvantages: Slow throughput, read this was a bad idea Master runs on a dedicated node Other alternatives UseKatib spins up a PyTorch streaming function with S3(boosted transfer speed)set number of GPU spot nodes Every pod starts a claim is made and downloads data to an EBS Give up and switch to SageMakerFSx Lustre system is generated for the pod Anyone with experience in these technologies I would really appreciate hearing your thoughts. submitted by /u/thewineiswater [link] [comments]  ( 1 min )

I am getting the same prediction for different inputs. I am trying to use a regressional neural network. Since data is huge, I am training one example at a time. Here is a simplified version of my code. model = Sequential() model.add(Dense(10000, input_dim=212207, kernel_initializer='normal', activation='relu')) model.add(Dense(100, activation='relu')) model.add(Dense(1, kernel_initializer='normal')) model.compile(loss='mean_squared_error', optimizer='adam') for i in range(10000000): #X is input with 212207 values #Y is a output value if i<6000000: model.fit(X.transpose(), Y, epochs=30, batch_size=1, verbose=0) else: prediction=model.predict(X.transpose()) I made sure that I am training on different examples and trying predictions on different examples. I am still getting the same prediction value for all testing inputs. I think I made some mistake in defining the model for regression neural network. Can you please check if the code is correct? submitted by /u/exoplanet_hunter [link] [comments]  ( 1 min )

Introduction I was puzzled the first time I saw bilinear transformations, also known as Möbius transformations. I was in a class where everything had been abstract and general, and suddenly thing got very concrete and specific. I wondered why we had changed gears, and I wondered how there could be much to say about something […] Fixed points of bilinear transformations first appeared on John D. Cook.  ( 2 min )

This post looks at how to partition complexity between definitions and theorems, and why it’s useful to be able to partition things more than one way. Quadratic equations Imagine the following dialog in an algebra class. “Quadratic equations always have two roots.” “But what about (x – 5)² = 0. That just has one root, […] Partitioning complexity first appeared on John D. Cook.  ( 4 min )

Posted by Alex Olwal, Research Scientist, Google Augmented Reality and Artem Dementyev, Hardware Engineer, Google Research As consumer electronics and internet-connected appliances are becoming more common, homes are beginning to embrace various types of connected devices that offer functionality like music control, voice assistance, and home automation. A graceful integration of devices requires adaptation to existing aesthetics and user styles rather than simply adding screens, which can easily disrupt a visual space, especially when they become monolithic surfaces or black screens when powered down or not actively used. Thus there is an increasing desire to create connected ambient computing devices and appliances that can preserve the aesthetics of everyday materials, while providing …  ( 7 min )

Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, and data from any document or image. Amazon Textract now offers the flexibility to specify the data you need to extract from documents using the new Queries feature within the Analyze Document API. You don’t need to know the structure of the […]  ( 11 min )

Yes! You read the heading right. There’s indeed a difference between loss functions and Metrics in the field of Machine Learning. However…  ( 2 min )

MIT CSAIL scientists created an algorithm to solve one of the hardest tasks in computer vision: assigning a label to every pixel in the world, without human supervision.  ( 7 min )

A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, cyclists, and pedestrians in real-time.  ( 7 min )

Your next trip to the dentist might offer a taste of AI. Pearl, a West Hollywood startup, provides AI for dental images to assist in diagnosis. It landed FDA clearance last month, the first to get such a go-ahead for dentistry AI. The approval paves the way for its use in clinics across the United Read article > The post Tooth Tech: AI Takes Bite Out of Dental Slide Misses by Assisting Doctors appeared first on NVIDIA Blog.  ( 4 min )

The gods must be smiling this GFN Thursday — God of War today joins the GeForce NOW library. Sony Interactive Entertainment and Santa Monica Studios’ masterpiece is available to stream from GeForce NOW servers, across nearly all devices and at up to 1440p and 120 frames per second for RTX 3080 members. Get ready to Read article > The post GFN Thursday Is Fit for the Gods: ‘God of War’ Arrives on GeForce NOW appeared first on NVIDIA Blog.  ( 3 min )

Hi, I made a video explaining the ideas behind building a Dense Passage Retriever(DPR). Whenever we talk about retrievers, we mostly refer to the DPR formulation which appeared in this paper. A lot of publicly available implementations also use this formulation. In a previous video, we discussed how to use the DPR End-to-End QA system which uses DPR with a QA model. In this video, we solely focus on retrievers and the ideas behind building them. The implementation is quite similar to retrievers pre-trained with Inverse Close Task. This video is part 8 of 9 video series on Open-domain question answering using Dense retrievers. Thanks for the support and I will appreciate any feedback. https://www.youtube.com/watch?v=w61p0HLo7gc submitted by /u/infiniteakashe [link] [comments]  ( 1 min )

submitted by /u/cjmodi306 [link] [comments]

We are searching for trustworthy volunteers with some free time who would like to contribute to a digital Ukrainian volunteer project. Our system heavily relies on an image recognition system with a number of specialized filters involvg facial recognition, object recognition, logo detection, photoshop detection etc. People with professional experience with any of these things is preferred, but novice ML people are welcome to join us in a different capacity. DM to learn more about the project, glad to discuss the details with you. submitted by /u/eelgirl [link] [comments]  ( 1 min )

If you are employed as a data scientist and have survived (or strived!) in your position for more than a year, chances are you are at least a good data scientist. This is particularly true if you were promoted. The difference between a mediocre and a good data scientist will be the topic of a… Read More »18 Differences Between Good and Great Data Scientists The post 18 Differences Between Good and Great Data Scientists appeared first on Data Science Central.  ( 6 min )

submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

Hello Everyone, I apologize ahead of time if posts like this aren't looked well upon on this sub, but I couldn't find rules against this and I also think this is the best, most niche sub for my question. I also made a new account just to be safe haha. ​ Anyways, I will be graduating this spring with a BS in Computer Science and a BA in Mathematics. I have been researching Machine Learning since my sophomore year (adversarial machine learning) under a professor at my university and recently took upon a second, concurrent research position in RL since last summer. ​ My goal is to get into a PhD program at a higher level than my current university (my current university is good, but doesn't really have much of an AI focus as I've already taken all the AI grad courses as an undergrad). I'm…  ( 3 min )

Hey everyone, I've recently started working on a task allocation problem using RL. I'd just like to make sure my thinking is correct on how to best approach the problem. At the moment, we have (effectively) a graph traversal sim for n number of agents, where the goal is to minimize the total distance over an episode, as determined by setting the correct tasks. The task supplied to each agent will determine the route that is taken, and therefore the distance. The current idea is to supply an input graph that also contains information on the current location of the agents. A second input would be the set of available tasks. The expected output would be done through a pointer network, where we produce a reordered set of the tasks in descending order of optimality. When step is called, the sim runs until a new task is needed (agent completes it's route). ​ In general, does anyone know a good way to represent the inputs and output of this problem? A pointer network seems like it could work to produce actions, but if I need to do a forward pass for every agent, it seems that there would be no consideration of other agents when determining tasks (We shouldn't have 2 agents doing the same task). For the graph representation, a graph nn seems like an obvious choice, but I just wanted to see if anyone had any insight on why they may or may not be used. submitted by /u/asdfsflhasdfa [link] [comments]  ( 1 min )

Can you name any good universities (with high acceptance rate) which are working on reinforcement learning for robotics and also accept students from other branches (i.e. Electrical, Mechanical Engineering). submitted by /u/Better-Ad8608 [link] [comments]  ( 1 min )

In the standard intro to RL book, I have read that any MDP that has finite actions and states is a finite MDP. But that limit is subjective. So there are approximately 1045. If I limit myself to 105 states, can I say that chess isn't a finite MDP? submitted by /u/BraveProfessional656 [link] [comments]  ( 1 min )

I am currently studying use cases of RL in finance/banking/insurance and I am keen to understand what are its advantages and disadvantages than traditional methods. submitted by /u/kachua26 [link] [comments]  ( 1 min )

Posted by Chen-Yu Lee and Chun-Liang Li, Research Scientists, Google Research, Cloud AI Team Form-based document understanding is a growing research topic because of its practical potential for automatically converting unstructured text data into structured information to gain insight about a document’s contents. Recent sequence modeling, which is a self-attention mechanism that directly models relationships between all words in a selection of text, has demonstrated state-of-the-art performance on natural language tasks. A natural approach to handle form document understanding tasks is to first serialize the form documents (usually in a left-to-right, top-to-bottom fashion) and then apply state-of-the-art sequence models to them. However, form documents often have more complex layouts …  ( 8 min )

I stumbled upon this JD from Hitachi Energy, which mentions PINN in the section of preferred background: https://www.linkedin.com/jobs/view/2923292435/ Is PINN gaining more attention? And are there more players? submitted by /u/Kohomologia [link] [comments]  ( 1 min )

Hi all I'm keeping an eye on state of the art in quantum hardware, but what about software? I can think of many questions and maybe some of you are in the field. What should be the impact of quantum on ML/DL, realistically? What might be a roadmap for the software? And would quantum simulators do already have some benefits on AI? What are the best projects out there? I've seen many but haven't been very convinced submitted by /u/IntelligentHat1657 [link] [comments]  ( 2 min )

Hi, ML freelancers. I'm starting a freelancing marketplace, tailored only for AI talents, and I especially care about the welfares of freelancers, and plan to add these: (1) you will be more treated as the employees of the platform, thus we provide training(for all), potentially health care plan(for people have stably worked >20 hours a week), and career advance plan, mentors from experienced freelancers where you get to learn (2) open discussion between employers and you so that you can scope the project better, set a reasonable rate, and timeline (3) we potentially provide MLOPs tool to improve your productivity. (4) we avoid global competition by matching business only with local region-freelancer or areas that are more expensive. How attractive do you think this will be? And any of these benefits already been provided by upwork, freelancer, toptal, fierr? submitted by /u/meame2010 [link] [comments]  ( 2 min )

Diffusion models have been behind a recent string of impressive generative results, including OpenAI's DALL-E 2. They’re powered by a simple yet expressive core mechanism. New video covering how they work: https://youtu.be/fbLgFrlTnGU submitted by /u/ariseff [link] [comments]

Interested in how DoorDash maintains a well performing and diverse selection in the numerous markets they operate in despite entering the delivery market relatively late ? I had the opportunity to collaborate in this project which involved building a number of models that measured customer preferences, identified market cuisine categories, and predicted merchants' performance on the platform. I wanted to share the approach and some of the technical details with the ML community to get feedback on what we can improve and to show this cool use case to others working on similar sales enablement based models. Check out the blog post I wrote and let me know what you think of our approach. Building the Model Behind DoorDash’s Expansive Merchant Selection submitted by /u/EfficientString7431 [link] [comments]  ( 1 min )

I took a break from deep learning( starting from last October) , now i want to get back, start with a new project and read papers . Where should i focus ? Should i keep working on vision transformers or maybe start something on geometric deep learning . What's hot and what's going on ? submitted by /u/ovotheking [link] [comments]  ( 1 min )

https://github.com/sovit-123/yolov1_pytorch_voc07 ​ Also, I write about Deep Learning and Machine Learning on https://debuggercafe.com/ Please check it out and let me know if somebody wants any blog posts on a specific topic. submitted by /u/sovit-123 [link] [comments]

Hi all!, Really excited to share a project we've been working on and get your feedback! We've made: Programmatic — an NLP annotation tool for building large labeled datasets for NLP without manual annotation Programmatic is like a REPL for data annotation. You: 1. Write simple rules/functions that can approximately label the data 2. Get near-instant feedback across your entire corpus 3. Iterate and improve your rules Finally, it uses a Bayesian label model [1] to convert these noisy annotations into a single, large, clean dataset, which you can then use for training machine learning models. You can programmatically label millions of datapoints in the time taken to hand-label hundreds. What we do differently from weak supervision packages like Snorkel/skweak[1] is to focus on UI to …  ( 2 min )

There are many projects promoting in this sub, you may like or dislike. And if any of my posts you dislike, allow me to apologize first. However, it gets me to think. Several years ago I'm a moderator in a quite large forum, because I don't have enough time to fulfill my responsibilities, then I decided to retire (yes, they can, and I remained as the vip user which only retired moderators can be). This is a large community, a machine learning community. Besides continuously removing some of these posts, and no clear rules on it, can we do any better? We got all the data, and we just cannot train the model? Here are my three proposal, and please give some excellent ideas besides my poor ones: Self promoting post should have values other than itself, and not having annoying contents Self promoting project can be used as a tool in a non self promoting posts, as long as the posts creates valuable contents and the promoting is not obvious and annoying. Depends on the number of new project posts, Weekly/Daily project post can be created by moderator and pinned to the top. All the promoting content goes into the comment. We can explore and upvotes. Here are some illustrations: 1. Direct Promoting Post ​ 2. Indirect Promoting Post ​ Weekly/Daily Promoting Post by Moderator, Pinned to Top, Comments by project owner, upvotes/downvotes by us Which do you think is acceptable? Or you have better ideas? Leave a comment. It's a machine learning sub, don't make machine to solve it better than us. View Poll submitted by /u/Remote_Cancel_7977 [link] [comments]  ( 3 min )

Hey folks, I wrote a mini project based on JAX for optical communications signal processing. https://github.com/remifan/commplax I have a research article as a use case demo, https://remifan.github.io/gdbp_study/article.html This tool essentially implements adaptive DSP equalizers as stateful NN layers (thanks to Jax's explicit stateful syntax) implements compositor interfaces from scratch to wrap up those stateful layers with other regular NN layers so that they can be trained together Homebrew serial compositions of stateful layers It is a fun project for me and I feel JAX really elegantly fits this research use. What do you think about JAX? I appreciate your comments:) submitted by /u/StreetPrice1909 [link] [comments]  ( 1 min )

Hi guys, I've been working on a machine learning project and I wanted to see how hardware resources are being used when I run inference on let's say 1000 images. How could i calculate the CPU(running inference on CPU)/RAM workload in that timeframe? I'm running it on a Linux Ubuntu VM. Thanks in advance! submitted by /u/Fifi0912 [link] [comments]  ( 1 min )

I'm working on a framework Mercury for converting Python notebooks into interactive web apps. It can add widgets to the notebook based on the YAML configuration. End-user can tweak widgets values and execute the notebook. The resulting notebook can be downloaded as single-file HTML. Simple. The framework is built on Django+React. It is easy to deploy to Heroku or other cloud services. Recently, I made it possible to deploy it to Hugging Face Spaces (faster and larger machines than on free tier Heroku). The process of deployment is simple. You need to create a Gradio app on Spaces (my framework is not supported, yet ;) ). You need to add the app.py file that will run the Mercury server and upload the notebook. You can check the details in the docs. The HF Space with example notebook https://huggingface.co/spaces/pplonski/deploy-mercury submitted by /u/pp314159 [link] [comments]  ( 1 min )

I would like to test the architecture from the following paper with a different dataset: https://www.mdpi.com/2072-4292/13/19/3834 The authors state that their objective function is the following: https://preview.redd.it/u78f27jb6nu81.png?width=1027&format=png&auto=webp&s=32790a67ec829a1e79b252edd0714b8b3b5a7f4e Where: -x is the real grayscale image. -s is its downsampled version, which should be used both as the initial imput of the generator performing the super-resolution and as a first conditional variable in the learning process. -e is another two-dimensional array containing values for a second additional conditional variable. The authors, however, state that this should be implemented by using two separate conditional adversarial losses, one for each of the conditional variables. To clarify, the first adversarial loss should be: AdvLoss1(ParametersG, ParametersD) = - Log(Discriminator(x,s) - Log(1-Discriminator(Generator(s),s) While the second would be: AdvLoss2(ParametersG, ParametersD) = - Log(Discriminator(x,e) - Log(1-Discriminator(Generator(s),e) Which should be then summed up for the backward pass. In my pytorch implementation, however, I have only been able to set up a unique adversarial loss, which could be defined as: CurrentAdvLoss(ParametersG, ParametersD) = - Log(Discriminator(x,(s,e)) - Log(1-Discriminator(Generator(s),(s,e)) I have tried to implement implemented as follows:(simplified version) which I calculate in the following training loop (simplified version, from the same question asked in the Pytorch forum) as errD and errG after conditioning the network on both s and e at the same time: https://discuss.pytorch.org/t/conditional-gan-with-multiple-adversarial-losses/149627 My question is, is there a way to modify the following loop to obtain outputs that have been separately conditioned only first on s and then on e and thus calculate the two separate adversarial losses originally proposed by the authors instead? submitted by /u/Franken91 [link] [comments]  ( 2 min )

This is the discussion for accepted/rejected papers in IJCAI 2022. Results are supposed to release today. submitted by /u/errohan400 [link] [comments]  ( 1 min )

Paper: https://arxiv.org/abs/2204.07953v1 Code: https://github.com/decurtoydiaz/learning_with_signatures Tangential resources of interest: https://arxiv.org/abs/1905.08494, https://en.wikipedia.org/wiki/Rough_path#Signature, and https://labelerrors.com/ Personally, I believe from their code on Github, they have a possible data leakage (in the same vein of the current issue raised there) as well as an accuracy of 100% on a test set is fishier than a fish market. However, I am very curious to hear from the court of public opinion. How is everyone feeling about this? submitted by /u/blingblingbeepbeep [link] [comments]  ( 4 min )

I have a dataset of points cloud where each point in the point cloud has a variable. I am trying to relate the local geometry features to that point variable by using FPFH, This means I am generating my own features from the dataset by first using an area of n-points to compute normal-vector estimations and from x normal vector estimations to compute the FPFH. However, the numbers x and n are arbitrary and other combinations might describe the target variable better. So I wanted to know if there was a method to evaluate how good a given x and n value are at describing the target variable. I considered the correlation between the features (n,x) and the target variable but I read that this assumes linear combination redundancy. I am using scikit-learn. So basically I have features X(x,n) and a target variable Y. Which x and n, in the feature space X(x,n), describes the target variable, Y, best. I want to do it before the training because when I try to train it with my random forest regressor it takes 3-4 hours and I want to test for more combinations. submitted by /u/Neo-Rushdian [link] [comments]  ( 1 min )